{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "import scipy.stats as st\n",
    "import sklearn.metrics as met\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn.preprocessing as prep\n",
    "import time\n",
    "\n",
    "%matplotlib inline\n",
    "title = \"PPD\"\n",
    "path = \"C:/Users/recre/OneDrive/Stat\"\n",
    "icy = 'target'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PPD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initiate Data\n",
    "\n",
    "Read data from orginal data files, save them into database which is easier to reload"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Master Data\n",
    "Input:\n",
    "* Training Master Data:\n",
    "    * PPD_dat_1.csv: First-round training set of Master data\n",
    "    * PPD_dat_2.csv: First-round validation set of Master data\n",
    "    * PPD_dat_3.csv: Second-round training set of Master data\n",
    "    * PPD_dayt_2_1.csv: First-round public validation set of Y Labels\n",
    "    * PPD_dayt_2_2.csv: First-round private validation set of Y Labels\n",
    "* Validation Master Data:\n",
    "    * PPD_dav.csv: Second-round validation set of Master data\n",
    "    \n",
    "Output:\n",
    "* da: master data\n",
    "* irt, irv: sample indices of training and validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Read_concat_csv(file, par_csv = {}):\n",
    "    da = pd.concat(map(lambda x: pd.read_csv(x, **par_csv), file))\n",
    "    return(da)\n",
    "def Del_string(xstr):\n",
    "    xstrc = xstr.strip().strip(\"市\").strip(\"省\")\n",
    "    if(xstrc == \"\"):\n",
    "        xstrc = np.nan\n",
    "    return(xstrc)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "par_csv = dict(index_col = 0, encoding = \"GB18030\", parse_dates = [\"ListingInfo\"], na_values = [-1], \n",
    "               converters = dict(zip(*[[\"UserInfo_{}\".format(i) for i in [9, 2, 4, 8, 20, 7, 19]], [Del_string]*7])))\n",
    "file_dat = [\"{}/{}_dat_{}.csv\".format(path, title, 1+x) for x in range(3)]\n",
    "file_dayt = [\"{}/{}_dayt_2_{}.csv\".format(path, title, x) for x in [1, 2]]\n",
    "file_dav = [\"{}/{}_dav.csv\".format(path, title)]\n",
    "dat = Read_concat_csv(file_dat, par_csv).fillna(Read_concat_csv(file_dayt, {\"index_col\": 0}))\n",
    "dav = Read_concat_csv(file_dav, par_csv)\n",
    "np.save(\"{}/{}_irt.npy\".format(path, title), list(dat.index))\n",
    "np.save(\"{}/{}_irv.npy\".format(path, title), list(dav.index))\n",
    "da = pd.concat([dat, dav])\n",
    "da.to_hdf(\"{}/{}_da.h5\".format(path, title), key = \"da\", complib = \"zlib\", complevel = 1, mode = \"w\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Historical Records Data\n",
    "Input:\n",
    "* LogInfo Data:\n",
    "    * PPD_daht_1_LogInfo.csv: First-round training set of LogInfo data\n",
    "    * PPD_daht_2_LogInfo.csv: First-round validation set of LogInfo data\n",
    "    * PPD_daht_3_LogInfo.csv: Second-round training set of LogInfo data\n",
    "    * PPD_dahv_LogInfo.csv: Second-round validation set of LogInfo data\n",
    "* UserupdateInfo Data:\n",
    "    * PPD_daht_1_Userupdate.csv: First-round training set of UserupdateInfo data\n",
    "    * PPD_daht_2_Userupdate.csv: First-round validation set of UserupdateInfo data\n",
    "    * PPD_daht_3_Userupdate.csv: Second-round training set of UserupdateInfo data\n",
    "    * PPD_dahv_LogInfo.csv: Second-round validation set of UserupdateInfo data\n",
    "    \n",
    "Output:\n",
    "* dah1: historical records data: LogInfo\n",
    "* dah2: historical records data: UserupdateInfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Read_History(file, icid, ictime, par_csv = {}):\n",
    "    '''Organize Time-Dependent Historical Records\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    file: a list of file name\n",
    "    icid: column name of id\n",
    "    ictime: a list of 2 column names: [basetime, recordtime]\n",
    "    par_csv: other parameters for pd.read_csv\n",
    "    '''\n",
    "    par = {\"parse_dates\": ictime}\n",
    "    par.update(par_csv)\n",
    "    dah = Read_concat_csv(file, par)\n",
    "    dahb = (dah.assign(Id = dah[icid], Time = (dah[ictime[1]] - dah[ictime[0]]).astype('timedelta64[D]')).set_index([\"Id\", \"Time\"])\n",
    "            .drop([icid]+ictime, axis = 1).sort_index())\n",
    "    return(dahb)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "source": [
    "dah1 = Read_History(file = [\"{}/{}_dah{}_LogInfo.csv\".format(path, title, x) for x in [\"t_1\", \"t_2\", \"t_3\", \"v\"]],\n",
    "                      icid = 'Idx', ictime = ['Listinginfo1', 'LogInfo3'])\n",
    "dah2 = Read_History(file = [\"{}/{}_dah{}_Userupdate.csv\".format(path, title, x) for x in [\"t_1\", \"t_2\", \"t_3\", \"v\"]],\n",
    "                      icid = 'Idx', ictime = ['ListingInfo1', 'UserupdateInfo2'],\n",
    "             par_csv = {\"converters\": {\"UserupdateInfo1\": lambda x: x.lower()}})\n",
    "dah1.to_hdf(\"{}/{}_dah1.h5\".format(path, title), key = \"dah\", complib = \"zlib\", complevel = 1, mode = \"w\")\n",
    "dah2.to_hdf(\"{}/{}_dah2.h5\".format(path, title), key = \"dah\", complib = \"zlib\", complevel = 1, mode = \"w\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reload Data\n",
    "Load data from database, then concatenate and summerize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "da = pd.read_hdf(\"{}/{}_da.h5\".format(path, title), key = \"da\")\n",
    "dah1 = pd.read_hdf(\"{}/{}_dah1.h5\".format(path, title), key = \"dah\")\n",
    "dah2 = pd.read_hdf(\"{}/{}_dah2.h5\".format(path, title), key = \"dah\")\n",
    "irt, irv = np.load(\"{}/{}_irt.npy\".format(path, title)), np.load(\"{}/{}_irv.npy\".format(path, title))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concatenate Historical Data with Master Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Clean_history(dah, iccat, name):\n",
    "    grp1 = dah.reset_index()[[\"Id\", \"Time\"]].groupby(\"Id\")\n",
    "    grp2 = dah.groupby(level = [\"Id\", \"Time\"]).first().reset_index()[[\"Id\", \"Time\"]].groupby(\"Id\")\n",
    "    dahc1 = pd.concat([grp1.first(), grp1.count()], axis = 1, ignore_index = True).rename(columns = {0:\"FirstTime\", 1:\"Count\"})\n",
    "    dahc1 = dahc1.assign(DayFreq = grp2.count()[\"Time\"]/(1-dahc1[\"FirstTime\"])).loc[da.index]\n",
    "    dahc2 = dah.reset_index().groupby([\"Id\"]+iccat).count().unstack(iccat)[\"Time\"].loc[da.index]\n",
    "    dahc = pd.concat([dahc1, pd.DataFrame({\"Cats\": dahc2.notnull().sum(axis = 1)}), dahc2], axis = 1).fillna(0)\n",
    "    dahc.columns = [\"{}_{}\".format(name, x) for x in dahc.columns]\n",
    "    return(dahc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(89999, 353)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dah1c = Clean_history(dah1, list(dah1.columns), name = \"Log\")\n",
    "dah2c = Clean_history(dah2, list(dah2.columns), name = \"Userupdate\")\n",
    "da = pd.concat([da, dah1c, dah2c], axis = 1)\n",
    "da.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summarize Data\n",
    "Get univariate summary statistics of data:\n",
    "* Non-NA values for all variables\n",
    "* The 5 most frequent values for all variables\n",
    "* Mean, standard error, and quantiles for numeric variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def Value_counts(das, nhead = 5):\n",
    "    tmp = pd.value_counts(das).reset_index().rename_axis({\"index\": das.name}, axis = 1)\n",
    "    value = pd.DataFrame(['value {}'.format(x+1) for x in range(nhead)], index = np.arange(nhead)).join(tmp.iloc[:,0], how = \"left\").set_index(0).T\n",
    "    freq = pd.DataFrame(['freq {}'.format(x+1) for x in range(nhead)], index = np.arange(nhead)).join(tmp.iloc[:,1], how = \"left\").set_index(0).T\n",
    "    nnull = das.isnull().sum()\n",
    "    freqother = pd.DataFrame({das.name: [das.shape[0]-nnull-np.nansum(freq.values), nnull]}, index = [\"freq others\", \"freq NA\"]).T\n",
    "    op = pd.concat([value, freq, freqother], axis = 1)\n",
    "    return(op)\n",
    "def Summary(da):\n",
    "    op = pd.concat([pd.DataFrame({\"type\": da.dtypes, \"n\": da.notnull().sum(axis = 0)}), da.describe().T.iloc[:,1:], \n",
    "                    pd.concat(map(lambda i: Value_counts(da.loc[:,i]), da.columns))], axis = 1).loc[da.columns]\n",
    "    op.index.name = \"Columns\"\n",
    "    return(op)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Summary(da).to_csv(\"{}/{}_summary_da.csv\".format(path, title))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean Data\n",
    "\n",
    "Input Data: \n",
    "* da: master data. types: Numeric, Categorical, Time, or a series of similar columns\n",
    "* daa*: appendix data for da\n",
    "    * PPD_daa.csv: additional description of data types\n",
    "    * cnd_da_city_0.csv: China city rank data\n",
    "    * cnd_da_city_1.csv: China province/city/county longitudinal/latitudinal data\n",
    "* ictype: a dict indicating types of and methods for columns of da\n",
    "\n",
    "Processing:\n",
    "1. Prepare daa and ictype for transforming da\n",
    "1. Transforming all of da columns into numeric according to ictype\n",
    "    * Numeric: Keep\n",
    "    * Non-numeric: \n",
    "        * With additional information: Mapping into numeric\n",
    "            * Ordinal: As numeric\n",
    "            * Time: Transform into days and cycles of year, month, week\n",
    "            * With information in appendix data: Mapping into numeric according to daa*\n",
    "        * Without additional information: Mapping into 0-1 dummy variables from one-hot encoding, combining or deleting low frequency categories\n",
    "    * A series of similar columns: Transform the column series into columns of summary statistics, keeping or deleting original columns\n",
    "    * Count NA numbers in different series of variables\n",
    "1. Deleting the columns with almost all identical or NA values\n",
    "1. Deleting the columns with serious collinearity with any column before\n",
    "\n",
    "Output Data:\n",
    "* dac: cleaned data. types: numeric\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def Col_group(ic, i = 0):\n",
    "    cols = pd.Series([x.split(\"_\")[i] for x in ic], index = ic)\n",
    "    return(cols)\n",
    "daa = pd.read_csv(\"{}/{}_daa.csv\".format(path, title), index_col = \"Column\")[\"Type\"]\n",
    "daacity0 = pd.read_csv(\"{}/cnd_da_city_{}.csv\".format(path, 0), encoding = \"GB18030\", index_col = 0)\n",
    "daacity1 = pd.read_csv(\"{}/cnd_da_city_{}.csv\".format(path, 1), encoding = \"GB18030\",\n",
    "                       converters = {\"Prov\": Del_string, \"City\": Del_string, \"District\": Del_string})\n",
    "daacity1= (pd.concat(map(lambda i: daacity1.drop_duplicates([i]).set_index([i]).iloc[:,2:], [\"Prov\", \"City\", \"District\"]))\n",
    "            .reset_index().drop_duplicates([\"index\"]).set_index([\"index\"]))\n",
    "daacity = daacity1.join(daacity0, how = \"left\")\n",
    "ictype = {\"y\": [\"target\"],\n",
    "          \"date\": [\"ListingInfo\"], \n",
    "          \"catmap\": [\"UserInfo_{}\".format(i) for i in [2, 4, 8, 20, 7, 19]],\n",
    "          \"catmapd\": [daacity]*6,\n",
    "          }\n",
    "ictype[\"cols\"] = Col_group(da.drop(ictype[\"y\"], axis = 1).columns)\n",
    "tmp = ictype[\"cols\"].index[ictype[\"cols\"] == \"ThirdParty\"]\n",
    "ictype[\"serials\"] = pd.Series([\"_\".join([x.split(\"_\")[i] for i in [0,1,3]]) for x in tmp], index = tmp)\n",
    "ictype[\"catbin\"] = list((set(daa.index[daa == \"Categorical\"])|set(da.columns[da.dtypes == \"O\"])) - set(ictype[\"catmap\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def Time_to_num(das):\n",
    "    tmp = pd.DatetimeIndex(das)\n",
    "    daop = pd.DataFrame(dict(zip(*[[\"{}_{}\".format(das.name, i) for i in [\"Day\", \"Year\", \"DayofYear\", \"DayofMonth\", \"DayofWeek\"]], \n",
    "                                   [(das - das.min()).astype('timedelta64[D]').astype(int), tmp.year, tmp.dayofyear, tmp.day, tmp.dayofweek]])),\n",
    "                        index = das.index)\n",
    "    return(daop)\n",
    "def Cat_map(das, damap, fillna = {\"CityRank\":6}):\n",
    "    daop = das.reset_index().set_index([das.name]).join(damap, how = \"left\").set_index(das.index.name).reindex(das.index).fillna(fillna)\n",
    "    daop.columns = [\"{}_{}\".format(das.name, i) for i in damap.columns]\n",
    "    return(daop)\n",
    "def Cat_to_bin(das, a = 0.01):\n",
    "    '''Transfrom a categorical column to onehotencoding'''\n",
    "    tmp = pd.value_counts(das)/das.shape[0]\n",
    "    cat = list(tmp.index[tmp > a])\n",
    "    enc = prep.OneHotEncoder(n_values = len(cat)+1, sparse = False)\n",
    "    xbin = enc.fit_transform(np.transpose(\n",
    "            [das.astype(\"category\").cat.set_categories(cat).cat.rename_categories(1+np.arange(len(cat))).astype(\"float\").fillna(0).values]))[:,1:]     \n",
    "    dabin = pd.DataFrame(xbin, columns = [\"{}_{}\".format(das.name, x) for x in cat], index = das.index)    \n",
    "    if(tmp[tmp <= a].sum() > a):\n",
    "        dabin = pd.concat([dabin, pd.DataFrame({\"{}_Others\".format(das.name):das.notnull()-dabin.sum(axis = 1)})], axis = 1)\n",
    "    if(dabin.shape[1] == 2):\n",
    "        dabin = pd.DataFrame({das.name: xbin[:,0]}, index = das.index)\n",
    "    return(dabin)\n",
    "def Append_col_name(da, name):\n",
    "    return(da.rename(columns = dict(zip(*[list(da.columns), [\"{}_{}\".format(x, name) for x in da.columns]]))))\n",
    "def ColS_fillna(da, cols, f = \"median\", allNA = 0):\n",
    "    dafill = getattr(da[cols.index].groupby(cols, axis = 1), f)()[cols]\n",
    "    dafill.columns = cols.index\n",
    "    daop = da[cols.index].fillna(dafill).fillna(allNA)\n",
    "    return(daop)\n",
    "def ColS_summary(da, cols, f = [\"median\", \"std\"]):\n",
    "    grp = da[cols.index].groupby(cols, axis = 1)\n",
    "    daop = pd.concat(map(lambda x: Append_col_name(getattr(grp, x)(), x), f), axis = 1)\n",
    "    return(daop)\n",
    "def Clean_data(da, ictype, a = 0.01):\n",
    "    '''Transform and clean columns according to types'''\n",
    "    dac = da.copy().replace([-np.inf, np.inf], np.nan).replace(\"不详\", np.nan)\n",
    "    dac.loc[:, \"UserInfo_20\"] = dac.loc[:, \"UserInfo_20\"].fillna(dac.loc[:, \"UserInfo_19\"])\n",
    "    datime = pd.concat(map(lambda i: Time_to_num(dac.loc[:,i]), ictype[\"date\"]), axis = 1)\n",
    "    dacatmap = pd.concat(map(lambda i: Cat_map(dac.loc[:,ictype[\"catmap\"][i]], ictype[\"catmapd\"][i]), range(len(ictype[\"catmap\"]))), axis = 1)\n",
    "    dacatmap = pd.concat([dacatmap.iloc[:,15:20], ColS_summary(dacatmap, \n",
    "        pd.Series([\"_\".join([x.split(\"_\")[i] for i in [0,2]]) for x in dacatmap.columns[:15]], index = dacatmap.columns[:15]))], axis = 1)\n",
    "    dacatbin  = pd.concat(map(lambda i: Cat_to_bin(dac.loc[:,i], a = a), ictype[\"catbin\"]+[ictype[\"catmap\"][-1]]), axis = 1)\n",
    "    daS = ColS_summary(dac, ictype[\"serials\"], [\"median\", \"std\", \"min\", \"max\", \"first\"]).fillna(0)\n",
    "    cols = Col_group(daS.columns, i = -1)\n",
    "    daS.loc[:,cols == \"max\"] = daS.loc[:,cols == \"max\"] - daS.loc[:,cols == \"median\"].values\n",
    "    dacount = ColS_summary(dac, ictype[\"cols\"], [\"count\"])\n",
    "    dac = pd.concat([dac.drop(ictype[\"date\"] + ictype[\"catmap\"] + ictype[\"catbin\"] + list(ictype[\"serials\"].index), axis = 1), \n",
    "                     datime, dacatmap, dacatbin, daS, dacount], axis = 1)\n",
    "    tmp = pd.concat(map(lambda i: Value_counts(dac.loc[:,i]), dac.columns))\n",
    "    dac = dac.loc[:, (tmp[\"freq 1\"] + tmp[\"freq NA\"])/dac.shape[0] < 1 - a]\n",
    "    dac = dac.drop(dac.columns[np.any(np.abs(np.tril(np.corrcoef(dac.rank(pct = True).fillna(0.5).values, rowvar = 0), -1)) > 0.99, axis = 0)], axis = 1)\n",
    "    return(dac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(89999, 389)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dac = Clean_data(da, ictype, a = 0.001)\n",
    "Summary(dac).to_csv(\"{}/{}_summary_dac.csv\".format(path, title))\n",
    "dac.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Sets for Model\n",
    "### Data Division and Standardization\n",
    "Divide and standardize X and Y from cleaned data for models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y = dac.loc[:, [icy]]\n",
    "icx = list(set(dac.columns) - set([icy]))\n",
    "x = dac.loc[:, icx]\n",
    "x = x.apply(lambda x: x.fillna(x.median()),axis=0)\n",
    "x = (x.rank(pct = True)-0.5/x.shape[0]).apply(st.norm.ppf)\n",
    "#x = (x - x.mean())/x.std()\n",
    "xv = x.loc[irv].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross Validation Set\n",
    "Divide sub training and validation sets by K-folds\n",
    "* functions for divide and create cross-validation folds\n",
    "* functions for training/validating models by cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Kfolds(x, k = 10, seed = 1):\n",
    "    np.random.seed(seed)\n",
    "    xL = np.array_split(np.random.choice(x, len(x), replace = False), k)\n",
    "    return(xL)\n",
    "def GroupSelect(xL, i = 0):\n",
    "    xLc = xL.copy()\n",
    "    ingrp = list(xLc.pop(i))\n",
    "    exgrp = sum([list(x) for x in xLc], [])\n",
    "    return(ingrp, exgrp)\n",
    "def TrainSet(x, y, irtL, ig = 0):\n",
    "    irt2, irt1 = GroupSelect(irtL, i = ig)\n",
    "    xt1, xt2 = x.loc[irt1].values, x.loc[irt2].values\n",
    "    yt1, yt2 = y.loc[irt1].values, y.loc[irt2].values\n",
    "    return(xt1, xt2, yt1, yt2)\n",
    "def CrossTrain(x, y, irtL, fmodel, **kwargs):\n",
    "    modelL = []\n",
    "    for i in range(len(irtL)):\n",
    "        xt1, xt2, yt1, yt2 = TrainSet(x, y, irtL, ig = i)\n",
    "        modelL.append(fmodel(xt1, xt2, yt1, yt2, seed = i, **kwargs))\n",
    "    return(modelL)\n",
    "def CrossValid(x, y, irtL, modelL):\n",
    "    yt2pL = []\n",
    "    for i in range(len(irtL)):\n",
    "        xt1, xt2, yt1, yt2 = TrainSet(x, y, irtL, ig = i)\n",
    "        yt2p = ModelPredict(xt2, modelL[i])\n",
    "        yt2pL.append(yt2p)\n",
    "    return(yt2pL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "irtL = Kfolds(irt, k = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Functions\n",
    "Functions for models to train, validate and predict Y from data\n",
    "### Model Evaluation (functions)\n",
    "Score function and predictor for evaluating machine learning models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Score(y, yp, f = met.roc_auc_score):\n",
    "    score = f(y, yp)\n",
    "    print(\"Score: {:.4f}\".format(score))\n",
    "    return(score)\n",
    "def ModelPredict(xv, fmodel):\n",
    "    if(type(fmodel) == xgb.core.Booster):\n",
    "        xv = xgb.DMatrix(xv, missing = np.nan)\n",
    "    if(type(fmodel) in [lm.logistic.LogisticRegression, lm.stochastic_gradient.SGDClassifier, ensm.bagging.BaggingClassifier,\n",
    "           ensm.weight_boosting.AdaBoostClassifier, ensm.forest.RandomForestClassifier, ensm.forest.ExtraTreesClassifier]):\n",
    "        yvp = fmodel.predict_proba(xv)[:,1]\n",
    "    else:\n",
    "        yvp = fmodel.predict(xv).flatten()\n",
    "    return(yvp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Types of Training Models (functions)\n",
    "Different model types for training from sub training and validation sets, random seed and model parameters\n",
    "\n",
    "Input:\n",
    "* xt1, yt1: sub training sets for data\n",
    "* xt2, yt2: sub validation sets for data\n",
    "* seed: random seed for the model\n",
    "* parmodel: parameters of the model\n",
    "\n",
    "Processing:\n",
    "1. Train the model\n",
    "1. Print validation score and running time\n",
    "\n",
    "Output:\n",
    "* model: trained machine learning model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Type: Scikit-learn Classifiers\n",
    "Support of modeltype:\n",
    "* lm: LogisticRegression, SGDClassifier\n",
    "* ensm: AdaBoostClassifier, RandomForestClassifier, ExtraTreesClassifier, BaggingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sklearn.linear_model as lm\n",
    "import sklearn.ensemble as ensm\n",
    "def Classifier(xt1, xt2, yt1, yt2, seed = 0, modeltype = lm.LogisticRegression, parmodel = {}):\n",
    "    timestart = time.time()\n",
    "    par = {\"random_state\": seed, 'n_jobs': -1, \"penalty\": \"l2\", \"C\": 0.003, \"class_weight\": 'balanced', 'solver': 'sag'}\n",
    "    par.update(parmodel)\n",
    "    model = modeltype(**par)\n",
    "    model.fit(xt1, yt1.flatten())\n",
    "    score = Score(yt2, model.predict_proba(xt2)[:,1:])\n",
    "    print(\"Time: {:.2f} seconds\".format(time.time() - timestart))\n",
    "    return(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Type: XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "def XGBoost(xt1, xt2, yt1, yt2, seed = 0, parmodel = {}):\n",
    "    timestart = time.time()\n",
    "    xyt1 = xgb.DMatrix(xt1, label = yt1, missing = np.nan)\n",
    "    xyt2 = xgb.DMatrix(xt2, label = yt2, missing = np.nan)\n",
    "    par = {'colsample_bylevel': 0.1, 'max_depth': 4, 'min_child_weight': 1, 'sub_sample': 1, \n",
    "           'eta': 0.1, \"seed\": seed, \"objective\": 'binary:logistic', 'eval_metric': 'logloss'}\n",
    "    par.update(parmodel)\n",
    "    par[\"max_depth\"] = int(par[\"max_depth\"])\n",
    "    parval = [(xyt1,'train'), (xyt2,'val')]\n",
    "    model = xgb.train(params = par, dtrain = xyt1, num_boost_round = 10000, evals = parval, \n",
    "                      early_stopping_rounds = int(2*np.sqrt(xt1.shape[1]/par[\"eta\"])))\n",
    "    score = Score(yt2, ModelPredict(xt2, model))\n",
    "    print(\"Time: {:.2f} seconds\".format(time.time() - timestart))\n",
    "    return(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Type: Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from keras.layers import Input, Dense, Dropout, BatchNormalization\n",
    "from keras.models import Model\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.regularizers import activity_l2\n",
    "from keras.constraints import maxnorm\n",
    "def DNN(xt1, xt2, yt1, yt2, seed = 0, parmodel = {}):\n",
    "    np.random.seed(seed)\n",
    "    timestart = time.time()\n",
    "    par = {\"nhidlayer\": 2, \"rdrop\": 0.5, \"nhidnode\": 500, \"outnode\": 300,\n",
    "           'optimizer':'sgd', \"batch_size\": 64, \"earlystop\": 3, \"maxnorm\": 4, \"l2\": 0}\n",
    "    par.update(parmodel)\n",
    "    layerin = Input(shape=(xt1.shape[1],))\n",
    "    layer = layerin\n",
    "    for i in range(par[\"nhidlayer\"]):\n",
    "        layer = Dense(par[\"nhidnode\"], init = 'glorot_normal', activation=\"relu\", W_constraint = maxnorm(par[\"maxnorm\"]))(layer)\n",
    "        layer = BatchNormalization()(layer)\n",
    "        layer = Dropout(par[\"rdrop\"])(layer)\n",
    "    layer = Dense(par[\"outnode\"], init = 'glorot_normal', activation=\"relu\", W_constraint = maxnorm(par[\"maxnorm\"]))(layer)\n",
    "    layer = BatchNormalization()(layer)\n",
    "    layer = Dropout(par[\"rdrop\"])(layer)\n",
    "    layerout = Dense(1, activation='sigmoid')(layer)\n",
    "    model = Model(input=layerin, output=layerout)\n",
    "    model.compile(loss='binary_crossentropy', optimizer=par['optimizer'])\n",
    "    model.fit(xt1.astype(\"float32\"), yt1.astype(\"float32\"), nb_epoch=100, batch_size=par[\"batch_size\"], validation_data = (xt2, yt2), \n",
    "              callbacks = [EarlyStopping(monitor='val_loss', patience=par[\"earlystop\"])])\n",
    "    score = Score(yt2, ModelPredict(xt2, model))\n",
    "    print(\"Time: {:.2f} seconds\".format(time.time() - timestart))\n",
    "    return(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Variable Selection (functions)\n",
    "Importance of variables for models in Y prediction from model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def PlotWeight(w, score, hl = 0):\n",
    "    fig = plt.figure()\n",
    "    w.plot(kind = \"bar\", grid = True, title = \"Score = {:.4}\".format(score))\n",
    "    plt.plot((0, w.shape[0]), (hl, hl), \"k-\")   \n",
    "    fig.show()\n",
    "def WeightModel(model):\n",
    "    if(type(model) == lm.logistic.LogisticRegression):\n",
    "        wic = pd.DataFrame({\"LR_beta\":model.coef_[0]}, index = x.columns)\n",
    "    elif(type(model) == xgb.core.Booster):\n",
    "        wic = (pd.concat([pd.Series(icx, index = ['f{}'.format(x) for x in range(len(icx))]), pd.Series(model.get_fscore())], axis = 1)\n",
    "               .set_index(0).sort_index().fillna(0)[1])\n",
    "        wic = wic/np.sum(wic)*wic.shape[0]\n",
    "    return(wic)\n",
    "def WeightCI(fweight, modelL):\n",
    "    w = pd.concat(map(fweight, modelL), axis = 1, keys = np.arange(len(modelL)))\n",
    "    wb = pd.concat([w.mean(axis = 1), w.std(axis = 1)], axis = 1, keys = [\"Mean\", \"Std\"])\n",
    "    wb[\"LowerCI\"] = wb[\"Mean\"] - wb[\"Std\"]*1.96/np.sqrt(len(modelL))\n",
    "    wb[\"UpperCI\"] = wb[\"Mean\"] + wb[\"Std\"]*1.96/np.sqrt(len(modelL))\n",
    "    return(wb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Comparasion and Composite (functions)\n",
    "Compare one/multiple models with cross validation and weighted average\n",
    "* functions for evaluate weights of models according to cross-validation scores for weighted-average prediction\n",
    "* functions for using weights for models to predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def ScoreWeight(score):\n",
    "    w = np.exp((score - np.mean(score))/np.min(np.std(score, axis = 0)))\n",
    "    w = np.sum(w/np.sum(w), axis = 0)\n",
    "    return(w)\n",
    "def CrossScore(y, yt2pM, irtL, w = 1):\n",
    "    score = []\n",
    "    for i in range(len(irtL)):\n",
    "        xt1, xt2, yt1, yt2 = TrainSet(y, y, irtL, ig = i)\n",
    "        yt2L = [yt2pM[j][i] for j in range(len(yt2pM))]\n",
    "        if(w is 1):\n",
    "            score.append(list(map(lambda x: Score(yt2, x), yt2L)))\n",
    "        else:\n",
    "            score.append(Score(yt2, np.array(yt2L).T @ w))\n",
    "    return(np.array(score))\n",
    "def CrossScoreAnalysis(y, yt2pM, irtL, w = [], labels = None):\n",
    "    scoreL = CrossScore(y, yt2pM, irtL)\n",
    "    if(len(w) == 0):    \n",
    "        w = ScoreWeight(scoreL)\n",
    "    score = CrossScore(y, yt2pM, irtL, w)\n",
    "    ScorePlot(np.vstack([score, scoreL.T]).T, labels)\n",
    "    return(scoreL, score, np.array(w))\n",
    "def ScorePlot(scoreL, labels):\n",
    "    scmean, scmstd = np.mean(scoreL[:,0]), np.std(scoreL[:,0])/np.sqrt(scoreL.shape[0])\n",
    "    plt.figure()\n",
    "    plt.boxplot(scoreL, labels = labels, showmeans = True)\n",
    "    plt.plot((0, scoreL.shape[1]+1), (scmean, scmean), \"g-\")\n",
    "    plt.plot((0, scoreL.shape[1]+1), (scmean - 1.96*scmstd, scmean - 1.96*scmstd), \"g--\") \n",
    "    plt.plot((0, scoreL.shape[1]+1), (scmean + 1.96*scmstd, scmean + 1.96*scmstd), \"g--\") \n",
    "    plt.title(\"Comp Mean Score: {:.4f},  95% CI: ({:.4f}, {:.4f}),  Folds: {}\".format(scmean, scmean - 1.96*scmstd, scmean + 1.96*scmstd, scoreL.shape[0]))\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "def CrossPredict(xv, modelL):\n",
    "    yvpL = []\n",
    "    for i in range(len(modelL)):\n",
    "        yvp = ModelPredict(xv, modelL[i])\n",
    "        yvpL.append(yvp)\n",
    "    return(yvpL)\n",
    "def ModelMPredict(yvpM, w = 1):\n",
    "    yvpcomp = np.array([np.mean(yvpM[i], axis = 0) for i in range(len(yvpM))]).T.dot(w).flatten()\n",
    "    return(yvpcomp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Pre-Training\n",
    "Training for model selection and preestimate of hyper-parameters\n",
    "### Single Models\n",
    "Train a single model from a single pair of sub training and validation sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xt1, xt2, yt1, yt2 = TrainSet(x, y, irtL, ig = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.7761\n",
      "Time: 6.79 seconds\n"
     ]
    }
   ],
   "source": [
    "model = Classifier(xt1, xt2, yt1, yt2, seed = 0, modeltype = lm.LogisticRegression, parmodel = \n",
    "                   {\"penalty\": \"l2\", \"C\": 0.003, \"class_weight\": 'balanced', 'solver': 'sag'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.7850\n",
      "Time: 143.23 seconds\n"
     ]
    }
   ],
   "source": [
    "model = XGBoost(xt1, xt2, yt1, yt2, seed = 0, parmodel = \n",
    "                {'colsample_bylevel': 0.07, 'eta': 0.05, 'max_depth': 3, 'lambda': 50, 'min_child_weight': 1.5, 'gamma': 0.2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 71999 samples, validate on 8000 samples\n",
      "Epoch 1/100\n",
      "71999/71999 [==============================] - 24s - loss: 0.3049 - val_loss: 0.2434\n",
      "Epoch 2/100\n",
      "71999/71999 [==============================] - 29s - loss: 0.2426 - val_loss: 0.2387\n",
      "Epoch 3/100\n",
      "71999/71999 [==============================] - 31s - loss: 0.2365 - val_loss: 0.2384\n",
      "Epoch 4/100\n",
      "71999/71999 [==============================] - 30s - loss: 0.2332 - val_loss: 0.2385\n",
      "Epoch 5/100\n",
      "71999/71999 [==============================] - 30s - loss: 0.2312 - val_loss: 0.2373\n",
      "Epoch 6/100\n",
      "71999/71999 [==============================] - 30s - loss: 0.2280 - val_loss: 0.2431\n",
      "Epoch 7/100\n",
      "71999/71999 [==============================] - 28s - loss: 0.2276 - val_loss: 0.2369\n",
      "Epoch 8/100\n",
      "71999/71999 [==============================] - 27s - loss: 0.2254 - val_loss: 0.2367\n",
      "Epoch 9/100\n",
      "71999/71999 [==============================] - 25s - loss: 0.2251 - val_loss: 0.2398\n",
      "Epoch 10/100\n",
      "71999/71999 [==============================] - 30s - loss: 0.2237 - val_loss: 0.2370\n",
      "Epoch 11/100\n",
      "71999/71999 [==============================] - 28s - loss: 0.2240 - val_loss: 0.2367\n",
      "Epoch 12/100\n",
      "71999/71999 [==============================] - 27s - loss: 0.2203 - val_loss: 0.2385\n",
      "Epoch 13/100\n",
      "71999/71999 [==============================] - 32s - loss: 0.2206 - val_loss: 0.2409\n",
      "Epoch 14/100\n",
      "71999/71999 [==============================] - 31s - loss: 0.2188 - val_loss: 0.2390\n",
      "Epoch 15/100\n",
      "71999/71999 [==============================] - 26s - loss: 0.2178 - val_loss: 0.2413\n",
      "Score: 0.7709\n",
      "Time: 440.17 seconds\n"
     ]
    }
   ],
   "source": [
    "model = DNN(xt1, xt2, yt1, yt2, seed = 0, parmodel = \n",
    "            {\"nhidlayer\": 1, \"rdrop\": 0.5, \"nhidnode\": 500, 'optimizer': \"rmsprop\", \"batch_size\": 64, \"dropout\": 0.5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.6995\n",
      "Time: 294.38 seconds\n"
     ]
    }
   ],
   "source": [
    "model = Classifier(xt1, xt2, yt1, yt2, seed = 0, modeltype = ensm.BaggingClassifier, parmodel = \n",
    "                   {\"base_estimator\":ensm.ExtraTreesClassifier(criterion='entropy', min_weight_fraction_leaf=0, max_depth = 6),\n",
    "                   \"n_estimators\": 500, \"max_features\": 1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Hyper-parameters Optimization\n",
    "Hyper-parameters optimization for models with cross validaion\n",
    "\n",
    "Warning: The process of hyper-parameters optimization may take a very long time = N(iterations)\\*K(folds)\\*T(single model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from hyperopt import fmin, tpe, hp, STATUS_OK, Trials\n",
    "import hyperopt.pyll.stochastic\n",
    "def ParColScore(par, fmodel = XGBoost):\n",
    "    icx = list(wcol.index[wcol.iloc[:,1] > par[\"a\"]])\n",
    "    x = dac.loc[:, icx]\n",
    "    x = x.apply(lambda x: x.fillna(x.median()),axis=0)\n",
    "    x = (x.rank(pct = True)-0.5/x.shape[0]).apply(st.norm.ppf)\n",
    "    modelL = CrossTrain(x, y, irtL, fmodel)\n",
    "    yt2pL = CrossValid(x, y, irtL, modelL)\n",
    "    score = np.mean(CrossScoreAnalysis(y, [yt2pL], irtL)[0])\n",
    "    return(-score)\n",
    "def ParModelScore(par, fmodel = XGBoost):\n",
    "    irtL = Kfolds(irt, k = 5)\n",
    "    modelL = CrossTrain(x, y, irtL, fmodel, parmodel = par)\n",
    "    yt2pL = CrossValid(x, y, irtL, modelL)\n",
    "    score = np.mean(CrossScoreAnalysis(y, [yt2pL], irtL)[0])\n",
    "    return(-score)\n",
    "def HpOpt(space, fhpscore, seed = 0, max_evals = 50):\n",
    "    def Obj(par):\n",
    "        print(pd.DataFrame(par, index = [seed]))\n",
    "        return({\n",
    "            'loss': fhpscore(par),\n",
    "            'status': STATUS_OK,\n",
    "            'loss_variance': 5e-5\n",
    "            })\n",
    "    np.random.seed(seed)\n",
    "    trials = Trials()\n",
    "    best = fmin(Obj,\n",
    "        space=space,\n",
    "        algo=tpe.suggest,\n",
    "        max_evals=max_evals,\n",
    "        trials=trials)\n",
    "    op = pd.concat([pd.DataFrame([sum(list(trials.trials[i][\"misc\"][\"vals\"].values()), []) for i in range(len(trials))],\n",
    "                                 columns = list(trials.trials[0][\"misc\"][\"vals\"].keys())),\n",
    "           pd.DataFrame({\"loss\": trials.losses()})], axis = 1)\n",
    "    return(op)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 9.283853539473838e-05}\n"
     ]
    }
   ],
   "source": [
    "space = {'C': hp.loguniform(\"C\", -10, -2)}\n",
    "print(hyperopt.pyll.stochastic.sample(space))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "parda = HpOpt(space, lambda x:ParModelScore(x, fmodel = Classifier), max_evals = 30).sort_values([\"loss\"])\n",
    "parda.to_csv(\"{}/{}_par_lgr.csv\".format(path, title))\n",
    "paropt = parda.iloc[0, :-1].to_dict()\n",
    "parda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'gamma': 0.2434874734418997, 'min_child_weight': 16.245731608503576, 'max_depth': 4.0, 'lambda': 10.653284672394888, 'colsample_bylevel': 0.07780665788770644}\n"
     ]
    }
   ],
   "source": [
    "space = {'max_depth': hp.quniform(\"max_depth\", 1.5, 4.5, 1),\n",
    "         'min_child_weight': hp.loguniform(\"min_child_weight\", -2, 4),\n",
    "         'gamma': hp.loguniform(\"gamma\", -6, 2),\n",
    "         'lambda': hp.loguniform(\"lambda\", 0, 6),\n",
    "         'colsample_bylevel': hp.uniform('colsample_bylevel', 0.04, 0.15)\n",
    "}\n",
    "print(hyperopt.pyll.stochastic.sample(space))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma    lambda  max_depth  min_child_weight\n",
      "0           0.103231  0.107672  1.758499        4.0         34.273935\n",
      "Score: 0.7864\n",
      "Time: 63.60 seconds\n",
      "Score: 0.7957\n",
      "Time: 70.17 seconds\n",
      "Score: 0.7727\n",
      "Time: 47.90 seconds\n",
      "Score: 0.7791\n",
      "Time: 53.02 seconds\n",
      "Score: 0.7689\n",
      "Time: 45.33 seconds\n",
      "Score: 0.7864\n",
      "Score: 0.7957\n",
      "Score: 0.7727\n",
      "Score: 0.7791\n",
      "Score: 0.7689\n",
      "Score: 0.7864\n",
      "Score: 0.7957\n",
      "Score: 0.7727\n",
      "Score: 0.7791\n",
      "Score: 0.7689\n"
     ]
    },
    {
     "data": {
      "image/png": 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vrRhKRNwEHFaXd1ldegkDd20V8y8ALmiQ/zhwwlAqa2Zm5eVneVWYYyhWVo6h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\nipmZdYQ7FDMz64j/DxVNTQcraS/2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d1003c1668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma      lambda  max_depth  min_child_weight\n",
      "0            0.05411  0.002601  394.391123        2.0          37.29148\n",
      "Score: 0.7875\n",
      "Time: 159.03 seconds\n",
      "Score: 0.8023\n",
      "Time: 197.84 seconds\n",
      "Score: 0.7735\n",
      "Time: 165.98 seconds\n",
      "Score: 0.7803\n",
      "Time: 160.58 seconds\n",
      "Score: 0.7679\n",
      "Time: 155.20 seconds\n",
      "Score: 0.7875\n",
      "Score: 0.8023\n",
      "Score: 0.7735\n",
      "Score: 0.7803\n",
      "Score: 0.7679\n",
      "Score: 0.7875\n",
      "Score: 0.8023\n",
      "Score: 0.7735\n",
      "Score: 0.7803\n",
      "Score: 0.7679\n"
     ]
    },
    {
     "data": {
      "image/png": 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bLjfQ6QaYNTDQ6QbYTnNSvgfsPWtW3VHDvWfNmuymmD2F++buxUNeXWwil/uV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\nwgHFzMza4n8AJ5ZFi7T2koAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d119d42fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma     lambda  max_depth  min_child_weight\n",
      "0              0.108  0.103667  75.388268        4.0          0.983615\n",
      "Score: 0.7873\n",
      "Time: 78.25 seconds\n",
      "Score: 0.7978\n",
      "Time: 93.50 seconds\n",
      "Score: 0.7727\n",
      "Time: 68.59 seconds\n",
      "Score: 0.7787\n",
      "Time: 73.84 seconds\n",
      "Score: 0.7694\n",
      "Time: 56.74 seconds\n",
      "Score: 0.7873\n",
      "Score: 0.7978\n",
      "Score: 0.7727\n",
      "Score: 0.7787\n",
      "Score: 0.7694\n",
      "Score: 0.7873\n",
      "Score: 0.7978\n",
      "Score: 0.7727\n",
      "Score: 0.7787\n",
      "Score: 0.7694\n"
     ]
    },
    {
     "data": {
      "image/png": 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EnC9pBdAfEeslHQjcHRFHNdimcyhNOIdiZeUcSveVNYeyBDhc0kRJ44AZwG31\njSSNB6YBtxaqHwWmStpDkkiJ/RV52W3A7Px4Vt16ZmZWMS0DSkRsBs4H7gIeBBZHxApJcySdV2h6\nOnBnRDxfWPffga8BS4H7AAGDv/i2AHiTpB+QAs1lHdgfa2HrlIRZuXhsVl9bOZSIuAM4sq7u6rry\nIrbetVWsvxS4tEH9U8DJw+msmZmVl396pcKcQ7Gycg6l+8qaQzEzM2vJAaXHeJ7ayspjs/ocUMzM\nrCOcQ6kyjdL0qF9/G67RGpvg8dmE/095GxYRo5OU37lPYbug0Rib4PFZNp7y6jGep7ay8tisPgcU\nMzPrCOdQKszfQ7Gy8vdQus/fQzEzs8pyQOkxnqe2svLYrD4HFDMz6wjnUCrMORQrK+dQus85FDMz\nqywHlB7jeWorK4/N6nNAMTOzjnAOpcKcQ7Gycg6l+5xDMTOzynJA6TGep7ay8tisPgcUMzPrCOdQ\nKsw5FCsr51C6zzkUMzOrLAeUHuN5aisrj83qayugSBqQtFLSKkkXN1h+kaSlku6VtFzSi5L6JB1R\nqF8q6WlJF+R15klam5fdK2mg0ztnZmajp2UORdIYYBVwErAOWALMiIiVTdqfClwYESc32M5aYEpE\nrJU0D3g2Iq5o8fzOoTThHIqVlXMo3VfWHMoU4KGIWB0Rm4DFwPQh2s8EbmhQfzLwo4hYW6gb1Z01\nM7Odp52AcjCwplBem+u2I2lPYAC4ucHid7B9oDlf0jJJ10oa30ZfbAd5ntrKymOz+sZ2eHunAfdE\nxMZipaTdgbcCcwvVVwEfjYiQ9HHgCuDdjTY6e/ZsJk2aBEBfXx+TJ0+mv78f2DoIXW6vvGzZsmG1\nhxq1Wnn673I1yjA6z+fxubVcq9VYuHAhwJbPy9HWTg5lKjA/IgZyeS4QEbGgQdtbgJsiYnFd/VuB\n9w1uo8F6E4HbI+KYBsucQ2nCORQrK+dQuq+sOZQlwOGSJkoaB8wAbqtvlKespgG3NtjGdnkVSQcW\nimcAD7TbaTMzK5+WASUiNgPnA3cBDwKLI2KFpDmSzis0PR24MyKeL64vaS9SQv6Wuk1fLul+SctI\ngeiDO7Af1qatUxJm5eKxWX1t5VAi4g7gyLq6q+vKi4BFDdb9GbBfg/pzhtVTMzMrNf+WV4U5h2Jl\n5RxK95U1h2JmZtaSA0qP8Ty1lZXHZvU5oJiZWUc4h1JhzqFYWTmH0n3OoZiZWWU5oPQYz1NbWXls\nVp8DipkGM2AzAAAGMElEQVSZdYRzKBXmHIqVlXMo3deNHEqnf23YRpl28nCZMGHnbt92XTt7bILH\nZ9k4oFTYSM7MpBoR/R3vi1mRx2Zvcg7FzMw6wjmUHuM5Zysrj83O8vdQmphfm9+0Xpdquz+3b96e\n+eXqj9u7fXFslqk/VW/fDb5C6TGep7ay8tjsLF+h2E43a1a3e2DWmMdm9fkKxcxsF+QrFDMzqywH\nlB7j30uysvLYrD4HFDMz6wjnUMzMdkHOodhON39+t3tg1pjHZvX5CqXH+F5/KyuPzc4q7RWKpAFJ\nKyWtknRxg+UXSVoq6V5JyyW9KKlP0hGF+qWSnpZ0QV5ngqS7JP1A0p2Sxnd658zMbPS0vEKRNAZY\nBZwErAOWADMiYmWT9qcCF0bEyQ22sxaYEhFrJS0AnoyIy3OQmhARcxtsz1coHST/XpKVlMdmZ5X1\nCmUK8FBErI6ITcBiYPoQ7WcCNzSoPxn4UUSszeXpwKL8eBFwentdNjOzMmonoBwMrCmU1+a67Uja\nExgAbm6w+B1sG2j2j4j1ABHxBLB/Ox22HVXrdgfMmqh1uwO2gzr9H2ydBtwTERuLlZJ2B94KbDel\nVdD0Ynf27NlMmjQJgL6+PiZPnkx/fz+w9ctQLrdXPuWUZdRq5emPyy4PlmfNKld/qlau1WosXLgQ\nYMvn5WhrJ4cyFZgfEQO5PBeIiFjQoO0twE0Rsbiu/q3A+wa3ketWAP0RsV7SgcDdEXFUg206h2Jm\nNkxlzaEsAQ6XNFHSOGAGcFt9o3yX1jTg1gbbaJRXuQ2YnR/ParKemZlVRFvfQ5E0AHyGFICui4jL\nJM0hXalck9vMAk6JiLPq1t0LWA28NiKeLdTvA9wEHJqXn1k/VZbb+QplmKSRnZT4dbbRMJLx6bE5\nfN24QvEXG3tMrVbbMv9qViYem53lgNKAA4qZ2fCVNYdiZmbWkgNKjxm8zdCsbDw2q88BxczMOsI5\nFDOzXZBzKGZmVlkOKD3G89RWVh6b1eeAYmZmHeEcipnZLsg5FDMzqywHlB7jeWorK4/N6nNAMTOz\njnAOxcxsF+QcipmZVZYDSo/xPLWVlcdm9TmgmJlZRziHYma2C3IOxczMKssBpcd4ntrKymOz+hxQ\nzMysI5xDMTPbBTmHYmZmldVWQJE0IGmlpFWSLm6w/CJJSyXdK2m5pBcl9eVl4yV9VdIKSQ9K+vVc\nP0/S2rzOvZIGOrtr1ojnqa2sPDarr2VAkTQG+CxwCnA0MFPS64ptIuL/RMSxEXEccAlQi4iNefFn\ngG9GxFHAG4AVhVWviIjj8t8dHdgfa2HZsmXd7oJZQx6b1dfOFcoU4KGIWB0Rm4DFwPQh2s8EbgCQ\n9ErgxIj4IkBEvBgRzxTajur8nsHGjRtbNzLrAo/N6msnoBwMrCmU1+a67UjaExgAbs5VrwF+IumL\neVrrmtxm0PmSlkm6VtL4EfTfzMxKotNJ+dOAewrTXWOB44DP5emwnwFz87KrgNdGxGTgCeCKDvfF\nGnjkkUe63QWzhjw2dwERMeQfMBW4o1CeC1zcpO0twIxC+QDg4UL5BOD2ButNBO5vss3wn//85z//\nDf+v1ed7p//G0toS4HBJE4HHgRmkPMk28pTVNODswbqIWC9pjaQjImIVcBLwn7n9gRHxRG56BvBA\noycf7fuozcxsZFoGlIjYLOl84C7SFNl1EbFC0py0OK7JTU8H7oyI5+s2cQHwFUm7Aw8D5+b6yyVN\nBl4CHgHm7PDemJlZ15T+m/JmZlYN/qZ8j5B0naT1ku7vdl/MiiQdIumf8xefl0u6oNt9spHxFUqP\nkHQC8BzwpYg4ptv9MRsk6UDgwIhYJukVwH8A0yNiZZe7ZsPkK5QeERH3ABu63Q+zehHxREQsy4+f\nI/2aRsPvulm5OaCYWWlImgRMBr7X3Z7YSDigmFkp5OmurwEfyFcqVjEOKGbWdZLGkoLJlyPi1m73\nx0bGAaW3CP8gp5XTF4D/jIjPdLsjNnIOKD1C0vXAd4AjJD0q6dxW65iNBknHk35h47cL/6+S/3+k\nCvJtw2Zm1hG+QjEzs45wQDEzs45wQDEzs45wQDEzs45wQDEzs45wQDEzs45wQDEzs45wQDEzs474\n/+m6xCWZ54ZAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d119dbab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma      lambda  max_depth  min_child_weight\n",
      "0           0.062041  2.815963  119.438209        4.0          0.320332\n",
      "Score: 0.7858\n",
      "Time: 94.08 seconds\n",
      "Score: 0.8012\n",
      "Time: 80.70 seconds\n",
      "Score: 0.7759\n",
      "Time: 71.71 seconds\n",
      "Score: 0.7810\n",
      "Time: 93.83 seconds\n",
      "Score: 0.7707\n",
      "Time: 93.64 seconds\n",
      "Score: 0.7858\n",
      "Score: 0.8012\n",
      "Score: 0.7759\n",
      "Score: 0.7810\n",
      "Score: 0.7707\n",
      "Score: 0.7858\n",
      "Score: 0.8012\n",
      "Score: 0.7759\n",
      "Score: 0.7810\n",
      "Score: 0.7707\n"
     ]
    },
    {
     "data": {
      "image/png": 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NsRibI3meichfbLSnXa3bHTBrotbtDtgOc1J+Atj14IO3zhreu2kTtZ6erfVm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\nAcXMzDrCAcXMzDri/wPrJfMpTXLmiQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d101e1e630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma    lambda  max_depth  min_child_weight\n",
      "0           0.103638  0.005734  9.649955        4.0          6.631517\n",
      "Score: 0.7829\n",
      "Time: 75.80 seconds\n",
      "Score: 0.7964\n",
      "Time: 90.46 seconds\n",
      "Score: 0.7720\n",
      "Time: 53.17 seconds\n",
      "Score: 0.7782\n",
      "Time: 62.85 seconds\n",
      "Score: 0.7707\n",
      "Time: 48.23 seconds\n",
      "Score: 0.7829\n",
      "Score: 0.7964\n",
      "Score: 0.7720\n",
      "Score: 0.7782\n",
      "Score: 0.7707\n",
      "Score: 0.7829\n",
      "Score: 0.7964\n",
      "Score: 0.7720\n",
      "Score: 0.7782\n",
      "Score: 0.7707\n"
     ]
    },
    {
     "data": {
      "image/png": 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0FOAWStnk9SAwvTA9jW3NVo3msX1z13HAfRHxGICka4DfBi4H1kvaOyLWS9oH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d102020080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma    lambda  max_depth  min_child_weight\n",
      "0           0.103523  0.003093  6.275718        4.0          8.628248\n",
      "Score: 0.7847\n",
      "Time: 75.67 seconds\n",
      "Score: 0.7987\n",
      "Time: 58.73 seconds\n",
      "Score: 0.7713\n",
      "Time: 53.58 seconds\n",
      "Score: 0.7779\n",
      "Time: 54.63 seconds\n",
      "Score: 0.7664\n",
      "Time: 54.16 seconds\n",
      "Score: 0.7847\n",
      "Score: 0.7987\n",
      "Score: 0.7713\n",
      "Score: 0.7779\n",
      "Score: 0.7664\n",
      "Score: 0.7847\n",
      "Score: 0.7987\n",
      "Score: 0.7713\n",
      "Score: 0.7779\n",
      "Score: 0.7664\n"
     ]
    },
    {
     "data": {
      "image/png": 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z7X4086ZNS5UbXvOmTevYe5gVjUbfHM779KJ87BzyGN/J15C3DUfEDuAi4E7g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\nCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d1000ba160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma     lambda  max_depth  min_child_weight\n",
      "0           0.121014  0.008721  27.970595        4.0          4.887325\n",
      "Score: 0.7877\n",
      "Time: 84.71 seconds\n",
      "Score: 0.7996\n",
      "Time: 81.38 seconds\n",
      "Score: 0.7762\n",
      "Time: 64.85 seconds\n",
      "Score: 0.7793\n",
      "Time: 80.12 seconds\n",
      "Score: 0.7694\n",
      "Time: 58.04 seconds\n",
      "Score: 0.7877\n",
      "Score: 0.7996\n",
      "Score: 0.7762\n",
      "Score: 0.7793\n",
      "Score: 0.7694\n",
      "Score: 0.7877\n",
      "Score: 0.7996\n",
      "Score: 0.7762\n",
      "Score: 0.7793\n",
      "Score: 0.7694\n"
     ]
    },
    {
     "data": {
      "image/png": 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CXwKWAd8FBFyTZy8EXiHpflKguaID22MtbB+SMCsX983q86NXqkxjv5qtMTy6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d100139470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel    gamma    lambda  max_depth  min_child_weight\n",
      "0           0.086997  0.12068  3.170096        3.0          3.656034\n",
      "Score: 0.7858\n",
      "Time: 76.91 seconds\n",
      "Score: 0.8007\n",
      "Time: 87.11 seconds\n",
      "Score: 0.7717\n",
      "Time: 49.78 seconds\n",
      "Score: 0.7789\n",
      "Time: 76.20 seconds\n",
      "Score: 0.7695\n",
      "Time: 53.17 seconds\n",
      "Score: 0.7858\n",
      "Score: 0.8007\n",
      "Score: 0.7717\n",
      "Score: 0.7789\n",
      "Score: 0.7695\n",
      "Score: 0.7858\n",
      "Score: 0.8007\n",
      "Score: 0.7717\n",
      "Score: 0.7789\n",
      "Score: 0.7695\n"
     ]
    },
    {
     "data": {
      "image/png": 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jIp7uyF7ZFnY+5JDNs4Yr16+n1tOzud6smzw2ty8tp7wkTQBWACcCa4ElwIyI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d1001b0d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma     lambda  max_depth  min_child_weight\n",
      "0           0.136027  1.481315  27.053301        4.0          0.652312\n",
      "Score: 0.7862\n",
      "Time: 91.25 seconds\n",
      "Score: 0.8006\n",
      "Time: 84.82 seconds\n",
      "Score: 0.7748\n",
      "Time: 70.28 seconds\n",
      "Score: 0.7795\n",
      "Time: 71.60 seconds\n",
      "Score: 0.7717\n",
      "Time: 65.75 seconds\n",
      "Score: 0.7862\n",
      "Score: 0.8006\n",
      "Score: 0.7748\n",
      "Score: 0.7795\n",
      "Score: 0.7717\n",
      "Score: 0.7862\n",
      "Score: 0.8006\n",
      "Score: 0.7748\n",
      "Score: 0.7795\n",
      "Score: 0.7717\n"
     ]
    },
    {
     "data": {
      "image/png": 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Hxr8pZjtw35xYPOTVZSrtboBZHZV2N8B2WkcMefl0pYXuB17U7kaY1eC+2VoD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\nvig2YwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d102bd6b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma     lambda  max_depth  min_child_weight\n",
      "0           0.059903  2.676418  284.85398        2.0          0.176539\n",
      "Score: 0.7837\n",
      "Time: 283.56 seconds\n",
      "Score: 0.7956\n",
      "Time: 167.38 seconds\n",
      "Score: 0.7716\n",
      "Time: 162.88 seconds\n",
      "Score: 0.7780\n",
      "Time: 157.65 seconds\n",
      "Score: 0.7640\n",
      "Time: 170.45 seconds\n",
      "Score: 0.7837\n",
      "Score: 0.7956\n",
      "Score: 0.7716\n",
      "Score: 0.7780\n",
      "Score: 0.7640\n",
      "Score: 0.7837\n",
      "Score: 0.7956\n",
      "Score: 0.7716\n",
      "Score: 0.7780\n",
      "Score: 0.7640\n"
     ]
    },
    {
     "data": {
      "image/png": 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+APClDtth0+N1tMc5Kej8F+lYvJ50a3o9oMwmXeA9RrrY/0RhvZbHPh0eB41/9QnwEUka\nJAWCScAXImJhw/JnksbmDiUFgo9HxKKR1pU0lXTFPy1/UW+MiE10SNJ80u18Y+2+2S5C0sHAVyLi\nlb2uy3iTtAfp4uaEKPy4sewk1ev8SK/rMlaSXgz8Q0QUf9B5HfCe2HH+bEJoG1Dy+Oxq0i2P60m9\nj9kRsapQ5jxSF/E8SfuRumoHkK5Kmq4raSFpAu1CSecAUyPi3I4r7oBiZlYqnUzKH0f69eyaiNhM\numVyVkOZYPhuk31IgWJLm3Vnkbpx5H9Hmsw3M7OS6+S24YMp3J5LGk89rqHMp0m3eK4nTSC9qYN1\nD4jh2zQfzLfkdiwizh9NeTMze2p167bhk4BlEXEQ6e6cz0h6Rpt1GrWfzDEzs9LqpIdyP4X7mkl3\ns9zfUOZt5B8LRsSPJf2E9OiPkdZ9UNIBEbFB0oE8+QeM20lyoDEzG4OI2OnfyY1GJwFlKXBYftbP\nA6Tb0BofYb6G9AO0byv9vwyHk26h3TTCuteQnpuzkHTvduMztbbr5E4068yCBQtYsGBBr6thtgO3\nze7KT/sYV20DSkRslXQmcAPDt/6ulDQvLY6LSY9sWCSp/kC390fEwwDN1s1lFgJXSKo/GfeN3dwx\na+7ee+/tdRXMmnLbrL6OnuUVEdfR8DTNiPh84fUDpHmUjtbN+Q8z/FgNMzOrOP8XwBPM0NBQr6tg\n1pTbZvV19Ev5XpIUZa+jmVnZSBr3SXn3UCaYWq3W6yqYNeW2WX0OKGZm1hUe8jIz2wV5yMvMzCrL\nAWWC8Ti1lZXbZvU5oJiZWVd4DsXMbBfkORQzM6ssB5QJxuPUVlZum9XngGJmZl3hORQzs12Q51DM\nzKyyHFAmGI9TW1m5bVafA4qZmXWF51DMzHZBnkMxM7PKckCZYDxObWXltll9HQUUSYOSVklaLemc\nJsvPlrRM0m2SVkjaIqkvL3tPzlsh6T2FdeZLWpfXuU3SYPd2y8zMxlvbORRJk4DVwAnAemApMDsi\nVrUofzLw3og4UdJRwOXAS4EtwHXAvIi4R9J84LGIuKjN+3sOZZSksQ2b+nO28TCW9um2OXplnUM5\nDrg7ItZExGZgCTBrhPJzSEEE4EjgexHx64jYCtwMnFYoO647O1FERMu/+fNbLzMbD26bu65OAsrB\nwNpCel3O24GkvYBB4MqcdQdwvKSpkvYGXgc8t7DKmZKWS7pE0pRR195G7fzza72ugllTbpvV1+1J\n+VOAWyJiI0AeFlsI/AfwDWAZsDWX/Szw/IjoBx4ERhz6MjOzcpvcQZn7gUML6UNyXjOzGR7uAiAi\nLgUuBZD0UXJvJyJ+Vij2j8C1rSowNDTE9OnTAejr66O/v5+BgQFg+M4QpztL1/PKUh+nnR5OD5Ss\nPtVK12o1Fi1aBLD9fDneOpmU3w24izQp/wDwP8CciFjZUG4KcA9wSEQ8Uch/dkT8TNKhpEn5GRHx\nqKQDI+LBXOZ9wEsj4vQm7+9J+S6SwB+nlZHbZnf1YlK+bQ8lIrZKOhO4gTRE9oWIWClpXlocF+ei\npwLXF4NJdqWkfYHNwLsi4tGcf6GkfmAbcC8wb+d3x9qrAQM9roNZMzXcNqutkyEvIuI64IiGvM83\npBcDi5us+6oW23xr59W0bpk7t9c1MGvObbP6/CwvM7NdUFl/h2JmZtaWA8oEU78rxKxs3DarzwHF\nzMy6wnMoZma7IM+h2FNuwYJe18CsObfN6nMPZYKRakQM9LoaZjtw2+wu91DMzKyy3EOZYPx4Cysr\nt83ucg/FzMwqywFlwqn1ugJmLdR6XQHbSQ4oE4yfl2Rl5bZZfZ5DMTPbBXkOxczMKssBZYLx85Ks\nrNw2q88BxczMusJzKGZmuyDPodhTzs9LsrJy26w+91AmGD8vycrKbbO7SttDkTQoaZWk1ZLOabL8\nbEnLJN0maYWkLZL68rL35LwVks4qrDNV0g2S7pJ0vaQp3dstMzMbb217KJImAauBE4D1wFJgdkSs\nalH+ZOC9EXGipKOAy4GXAluA64B5EXGPpIXAQxFxYQ5SUyPi3Cbbcw+li/y8JCsrt83uKmsP5Tjg\n7ohYExGbgSXArBHKzyEFEYAjge9FxK8jYitwM3BaXjYLWJxfLwZOHW3lzcysPDoJKAcDawvpdTlv\nB5L2AgaBK3PWHcDxeXhrb+B1wHPzsgMiYgNARDwI7D/66tvo1XpdAbMWar2ugO2kyV3e3inALRGx\nESAiVuWhrf8AHgeWAVtbrNuyszs0NMT06dMB6Ovro7+/n4GBAWD4x1BOd5Y+6aTl1GrlqY/TTtfT\nc+eWqz5VS9dqNRYtWgSw/Xw53jqZQ5kBLIiIwZw+F4iIWNik7FXAFRGxpMW2PgqsjYh/kLQSGIiI\nDZIOBG6KiCObrOM5FDOzUSrrHMpS4DBJ0yTtAcwGrmkslO/Smglc3ZD/7PzvocD/Ai7Li64BhvLr\nuY3rmZlZtbQNKHky/UzgBuBOYElErJQ0T9IZhaKnAtdHxBMNm7hS0h2kgPGuiHg05y8EXiPpLtId\nZBfs5L5YB+pdZLOycdusvo7mUCLiOuCIhrzPN6QXM3zXVjH/VS22+TBwYsc1NTOzUvMv5c3MdkFl\nnUOxXYifl2Rl5bZZfe6hTDB+XpKVldtmd7mHYmZmleUeygTj5yVZWbltdpd7KGZmVlkOKBNOrdcV\nMGuh1usK2E6qREBZUFvQMl/na4c/l29dnrmvLlV9XN7li22zTPWpevle8ByKmdkuyHMoZmZWWQ4o\nE4yfl2Rl5bZZfQ4oZmbWFZ5DMTPbBXkOxZ5yfl6SlZXbZvW5hzLB+HlJVlZum93lHoqZmVWWeygT\njPy8JCspt83ucg/FzMwqywFlwqn1ugJmLdR6XQHbSR0FFEmDklZJWi3pnCbLz5a0TNJtklZI2iKp\nLy97n6Q7JN0u6cuS9sj58yWty+vcJmmwu7tmzcyd2+samDXntll9bedQJE0CVgMnAOuBpcDsiFjV\novzJwHsj4kRJBwG3AC+MiN9I+grw9Yj4oqT5wGMRcVGb9/ccipnZKJV1DuU44O6IWBMRm4ElwKwR\nys8BLi+kdwOeLmkysDcpKNX15pGYZmbWdZ0ElIOBtYX0upy3A0l7AYPAlQARsR74OHAfcD+wMSK+\nWVjlTEnLJV0iacoY6m+j5OclWVm5bVbf5C5v7xTglojYCJDnUWYB04BNwNcknR4RlwGfBT4UESHp\nI8BFwDuabXRoaIjp06cD0NfXR39/PwMDA8BwI3S6s/Ty5ctLVR+nnXa6O+larcaiRYsAtp8vx1sn\ncygzgAURMZjT5wIREQublL0KuCIiluT0G4CTIuKdOf0W4GURcWbDetOAayPi6Cbb9ByKmdkolXUO\nZSlwmKRp+Q6t2cA1jYXykNVM4OpC9n3ADEl7ShJpYn9lLn9godxpwB1j2wUbDT8vycrKbbP6Ovql\nfL6l91OkAPSFiLhA0jxST+XiXGYuqTdyesO680lBaDOwDPjjiNgs6YtAP7ANuBeYFxEbmry3eyhd\n5OclWVm5bXZXL3oofvTKBOOD1srKbbO7HFCacEDpLj8vycrKbbO7yjqHYmZm1pYDyoRT63UFzFqo\n9boCtpMcUCps333TMMFo/mB05ffdt7f7aNU0Hm3T7bN8PIdSYeMx5uxxbRuL8Wo3bp+teQ7FzMwq\nywFlgqk/qsGsbNw2q88BxczMusJzKBXmORQrK8+h9J7nUMzMrLIcUCYYj1NbWbltVp8DipmZdYXn\nUCrMcyhWVp5D6T3PoZiZWWU5oEwwHqe2snLbrD4HFDMz6wrPoVSY51CsrDyH0nueQzEzs8pyQJlg\nPE5tZeW2WX0dBRRJg5JWSVot6Zwmy8+WtEzSbZJWSNoiqS8ve5+kOyTdLunLkvbI+VMl3SDpLknX\nS5rS3V0zM7Px1HYORdIkYDVwArAeWArMjohVLcqfDLw3Ik6UdBBwC/DCiPiNpK8AX4+IL0paCDwU\nERfmIDU1Is5tsj3PobTgORQrK8+h9F5Z51COA+6OiDURsRlYAswaofwc4PJCejfg6ZImA3sD9+f8\nWcDi/HoxcOpoKm5mZuXSSUA5GFhbSK/LeTuQtBcwCFwJEBHrgY8D95ECycaI+M9cfP+I2JDLPQjs\nP5YdsNHxOLWVldtm9U3u8vZOAW6JiI0AeR5lFjAN2AR8TdLpEXFZk3VbdlyHhoaYPn06AH19ffT3\n9zMwMAAMN0KnO0svX758VOWhRq1Wnvo7XY00jM/7uX0Op2u1GosWLQLYfr4cb53MocwAFkTEYE6f\nC0RELGxS9irgiohYktNvAE6KiHfm9FuAl0XEmZJWAgMRsUHSgcBNEXFkk216DqUFz6FYWXkOpffK\nOoeyFDhM0rR8h9Zs4JrGQvkurZnA1YXs+4AZkvaUJNLE/sq87BpgKL+e27CemZlVTNuAEhFbgTOB\nG4A7gSURsVLSPElnFIqeClwfEU8U1v0f4GvAMuAHgICL8+KFwGsk3UUKNBd0YX+sjeEhCbNycdus\nvo7mUCLiOuCIhrzPN6QXM3zXVjH/fOD8JvkPAyeOprJmZlZefpZXhXU6fnzBGWfwq9Wrd8jf8/DD\nOffii5usMfr3MCsaj7Y5mveZiHoxh9Ltu7yshH61ejULbr55h/wF418Vsydx29y1+FleE0yt1xUw\na6HW6wrYTqvEkJcvV7roJ8Dzel0JsybcNrtrAeM+5FWJgFL2OvZKp+PHCwYGmg8rzJzJgjZ31niM\n2sZiPNrmaN5nIirr71DMzMzacg+lytTZxccFwK/y63uB6fn1nsAOj3duxp+/jdZ4tU1w+2yhFz0U\nB5QKG0t3v1arFZ6D9NS8h9l4tM2xvs9E4YDShANKa36Wl5WVn+XVe55DMTOzynJAmWD8vCQrK7fN\n6nNAMTOzrvAcSoV5DsXKynMovec5FDMzqywHlAnG49RWVm6b1eeAYmZmXeE5lArzHIqVledQes9z\nKGZmVlkOKBOMx6mtrNw2q6+jgCJpUNIqSaslndNk+dmSlkm6TdIKSVsk9Uk6vJC/TNImSWfldeZL\nWpeX3SZpsNs7Z2Zm46ftHIqkScBq4ARgPbAUmB0Rq1qUPxl4b0Sc2GQ764DjImKdpPnAYxFxUZv3\n9xxKC55DsbLyHErvlXUO5Tjg7ohYExGbgSXArBHKzwEub5J/IvDjiFhXyBvXnTUzs6dOJwHlYGBt\nIb0u5+1A0l7AIHBlk8VvYsdAc6ak5ZIukTSlg7rYTvI4tZWV22b1Te7y9k4BbomIjcVMSbsDr+fJ\n/2fOZ4EPRURI+ghwEfCOZhsdGhpi+vTpAPT19dHf37/9/02oN0KnO0svX758VOWhRq1Wnvo7XY00\njM/7uX0Op2u1GosWLQLYfr4cb53MocwAFkTEYE6fC0RELGxS9irgiohY0pD/euBd9W00WW8acG1E\nHN1kmedQWvAcipWV51B6r6xzKEuBwyRNk7QHMBu4prFQHrKaCVzdZBs7zKtIOrCQPA24o9NKm5lZ\n+bQNKBGxFTgTuAG4E1gSESslzZN0RqHoqcD1EfFEcX1Je5Mm5K9q2PSFkm6XtJwUiN63E/thHRoe\nkjArF7fN6utoDiUirgOOaMj7fEN6MbC4ybq/BJ7dJP+to6qpmZmVmp/lVWGeQ7Gy8hxK75V1DsXM\nzKwtB5QJxuPUVlZum9XngGJmZl3hOZQK8xyKlZXnUHrPcyhmZlZZDigTjMeprazcNqvPAcXMzLrC\ncygV5jkUKyvPofSe51DMzKyyHFAmGI9TW1m5bVafA4qZmXWF51AqzHMoVlaeQ+k9z6GYmVllOaBU\nnDTav9qoyk+d2us9tKp6qtum22f5dPv/lLdxNJauvocIbDy4bU5MnkOZYHzQWlm5bXaX51DMzKyy\nHFAmnFqvK2DWQq3XFbCd1FFAkTQoaZWk1ZLOabL8bEnLJN0maYWkLZL6JB1eyF8maZOks/I6UyXd\nIOkuSddLmtLtnTMzs/HTNqBImgR8GjgJOAqYI+mFxTIR8bcR8ZKIOBY4D6hFxMaIWF3I/x3gF8BV\nebVzgW9GxBHAjXk9e4rNnz/Q6yqYNeW2WX1tJ+UlzQDmR8Qf5PS5QETEwhblvwzcGBFfaMh/LfDB\niDg+p1fIm+heAAAHnklEQVQBMyNig6QDSUHohU2250l5M7NRKuuk/MHA2kJ6Xc7bgaS9gEHgyiaL\n3wRcXkjvHxEbACLiQWD/TipsO8fPS7Kyctusvm7/DuUU4JaI2FjMlLQ78HrSMFcrLbshQ0NDTJ8+\nHYC+vj76+/sZGBgAhhuh052lly9fXqr6OO20091J12o1Fi1aBLD9fDneOh3yWhARgzndcshL0lXA\nFRGxpCH/9cC76tvIeSuBgcKQ100RcWSTbXrIy8xslMo65LUUOEzSNEl7ALOBaxoL5bu0ZgJXN9nG\nHJ483EXexlB+PbfFemZmVhFtA0pEbAXOBG4A7gSWRMRKSfMknVEoeipwfUQ8UVxf0t7AiQzf3VW3\nEHiNpLuAE4ALxr4b1qmhoVqvq2DWlNtm9fnRKxOMVCNioNfVMNuB22Z39WLIqxIBZf5N81kwsGCH\nZQtqCzj/5vN3yJ8/0+Vd3uVdfoKXX4ADSiP3ULrLD+CzsnLb7K6yTsrbLqXW6wqYtVDrdQVsJzmg\nmJlZVzigTDB+XpKVldtm9XkOxcxsF+Q5FHvK1R/VYFY2bpvV54BiZmZd4SEvM7NdkIe8zMysshxQ\nJhg/L8nKym2z+jzkNcH4eUlWVm6b3eVneTXhgNJdfryFlZXbZnd5DsXMzCrLAWXCqfW6AmYt1Hpd\nAdtJDihmZtYVDigTjJ+XZGXltll9npQ3M9sFeVLennJ+XpKVldtm9XUUUCQNSlolabWkc5osP1vS\nMkm3SVohaYukvrxsiqSvSlop6U5JL8v58yWty+vcJmmwu7tmZmbjqe2Ql6RJwGrgBGA9sBSYHRGr\nWpQ/GXhvRJyY04uAmyPiUkmTgb0j4lFJ84HHIuKiNu/vIS8zs1Eq65DXccDdEbEmIjYDS4BZI5Sf\nA1wOIOmZwPERcSlARGyJiEcLZcd1Z83M7KnTSUA5GFhbSK/LeTuQtBcwCFyZs54H/FzSpXlY6+Jc\npu5MScslXSJpyhjqb6Pk5yVZWbltVt/kLm/vFOCWiNhY2P6xwJ9FxK2SPgmcC8wHPgt8KCJC0keA\ni4B3NNvo0NAQ06dPB6Cvr4/+/n4GBgaA4Yk8pztLL168nKGh8tTHaafr6cWLh4NKGepTtXStVmPR\nokUA28+X462TOZQZwIKIGMzpc4GIiIVNyl4FXBERS3L6AOC7EfH8nH4lcE5EnNKw3jTg2og4usk2\nPYfSRX5ekpWV22Z3lXUOZSlwmKRpkvYAZgPXNBbKQ1YzgavreRGxAVgr6fCcdQLww1z+wMLqpwF3\njGkPzMysFNoOeUXEVklnAjeQAtAXImKlpHlpcVyci54KXB8RTzRs4izgy5J2B+4B3pbzL5TUD2wD\n7gXm7fTeWAdqwECP62DWTA23zWrzL+UnGP+fE1ZWbpvdVdYhL9uF+HlJVlZum9XnHsouSBrbRYk/\nZxsPY2mfbpuj5x6KdUVEtPy76aabWi4zGw9um7suBxQzM+sKD3mZme2CPORlZmaV5YAywdQf1WBW\nNm6b1eeAYmZmXeE5FDOzXZDnUMzMrLIcUCYYj1NbWbltVp8DipmZdYXnUMzMdkGeQzEzs8pyQJlg\nPE5tZeW2WX0OKGZm1hWeQzEz2wV5DsXMzCqro4AiaVDSKkmrJZ3TZPnZkpZJuk3SCklbJPXlZVMk\nfVXSSkl3SnpZzp8q6QZJd0m6XtKU7u6aNeNxaisrt83qaxtQJE0CPg2cBBwFzJH0wmKZiPjbiHhJ\nRBwLnAfUImJjXvwp4BsRcSRwDLAy558LfDMijgBuzOvZU2z58uW9roJZU26b1ddJD+U44O6IWBMR\nm4ElwKwRys8BLgeQ9Ezg+Ii4FCAitkTEo7ncLGBxfr0YOHUM9bdR2rhxY/tCZj3gtll9nQSUg4G1\nhfS6nLcDSXsBg8CVOet5wM8lXZqHwy7OZQD2j4gNABHxILD/WHbAzMzKoduT8qcAtxSGuyYDxwKf\nycNhvyQNdQE03n3gW7nGwb333tvrKpg15bZZfZM7KHM/cGghfUjOa2Y2ebgrWwesjYhbc/prQH1S\n/0FJB0TEBkkHAj9tVQFpXO982+UtXry4fSGzHnDbrLZOAspS4DBJ04AHSEFjTmOhfJfWTODN9bwc\nLNZKOjwiVgMnAD/Mi68BhoCFwFzg6mZvPt73UZuZ2dh09MNGSYOku7UmAV+IiAskzQMiIi7OZeYC\nJ0XE6Q3rHgNcAuwO3AO8LSI2SdoXuAJ4LrAGeGNhqMzMzCqm9L+UNzOzavAv5ScISV+QtEHS7b2u\ni1mRpEMk3Zh/+LxC0lm9rpONjXsoE4SkVwKPA1+MiKN7XR+zunxTzoERsVzSM4DvA7MiYlWPq2aj\n5B7KBBERtwCP9LoeZo0i4sGIWJ5fP056mkbT37pZuTmgmFlpSJoO9APf621NbCwcUMysFPJw19eA\n9+SeilWMA4qZ9ZykyaRg8qWIaPqbNCs/B5SJRez4yBuzMvgn4IcR8aleV8TGzgFlgpB0GfAd4HBJ\n90l6W6/rZAYg6RWkJ2z8fuH/VRrsdb1s9HzbsJmZdYV7KGZm1hUOKGZm1hUOKGZm1hUOKGZm1hUO\nKGZm1hUOKGZm1hUOKGZm1hUOKGZm1hX/H1mUPdtlVvNDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d101880898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma     lambda  max_depth  min_child_weight\n",
      "0           0.122874  0.782896  53.059224        3.0           0.80767\n",
      "Score: 0.7882\n",
      "Time: 99.21 seconds\n",
      "Score: 0.8012\n",
      "Time: 130.87 seconds\n",
      "Score: 0.7749\n",
      "Time: 75.28 seconds\n",
      "Score: 0.7813\n",
      "Time: 91.13 seconds\n",
      "Score: 0.7700\n",
      "Time: 68.41 seconds\n",
      "Score: 0.7882\n",
      "Score: 0.8012\n",
      "Score: 0.7749\n",
      "Score: 0.7813\n",
      "Score: 0.7700\n",
      "Score: 0.7882\n",
      "Score: 0.8012\n",
      "Score: 0.7749\n",
      "Score: 0.7813\n",
      "Score: 0.7700\n"
     ]
    },
    {
     "data": {
      "image/png": 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kUNr9S3kroV+vXMnAbbdtVT8w/k0x24L75o7FQ15dptbpBpg1Uet0A2ybVWLI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\n/wNWtjbPO19rPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d10a02d3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel    gamma      lambda  max_depth  min_child_weight\n",
      "0             0.0584  6.55951  116.272894        4.0         19.999858\n",
      "Score: 0.7861\n",
      "Time: 184.56 seconds\n",
      "Score: 0.7979\n",
      "Time: 179.26 seconds\n",
      "Score: 0.7752\n",
      "Time: 154.27 seconds\n",
      "Score: 0.7792\n",
      "Time: 142.88 seconds\n",
      "Score: 0.7665\n",
      "Time: 114.21 seconds\n",
      "Score: 0.7861\n",
      "Score: 0.7979\n",
      "Score: 0.7752\n",
      "Score: 0.7792\n",
      "Score: 0.7665\n",
      "Score: 0.7861\n",
      "Score: 0.7979\n",
      "Score: 0.7752\n",
      "Score: 0.7792\n",
      "Score: 0.7665\n"
     ]
    },
    {
     "data": {
      "image/png": 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9pEBzcRu2x5rY1iVhVi5um92vpTGUiLgVOKwm77M16auBq+vMeyFwYZ38J4CT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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d101edf748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma    lambda  max_depth  min_child_weight\n",
      "0            0.11273  1.196045  2.400008        4.0         16.976268\n",
      "Score: 0.7830\n",
      "Time: 59.06 seconds\n",
      "Score: 0.7969\n",
      "Time: 98.08 seconds\n",
      "Score: 0.7745\n",
      "Time: 49.36 seconds\n",
      "Score: 0.7782\n",
      "Time: 59.23 seconds\n",
      "Score: 0.7675\n",
      "Time: 40.04 seconds\n",
      "Score: 0.7830\n",
      "Score: 0.7969\n",
      "Score: 0.7745\n",
      "Score: 0.7782\n",
      "Score: 0.7675\n",
      "Score: 0.7830\n",
      "Score: 0.7969\n",
      "Score: 0.7745\n",
      "Score: 0.7782\n",
      "Score: 0.7675\n"
     ]
    },
    {
     "data": {
      "image/png": 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DMTOzynJAqbBA6RRtGI/aMNsHo/9tW6u+0RibHp/l44BSYSLS9f5wHrffPqz2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\nOKCYmVlHOKCYmVlH/H9YxFnO+yoQYAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d10a5686d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma    lambda  max_depth  min_child_weight\n",
      "0           0.055573  2.954674  1.654055        2.0         18.316502\n",
      "Score: 0.7860\n",
      "Time: 161.80 seconds\n",
      "Score: 0.7999\n",
      "Time: 139.83 seconds\n",
      "Score: 0.7722\n",
      "Time: 66.62 seconds\n",
      "Score: 0.7787\n",
      "Time: 96.65 seconds\n",
      "Score: 0.7690\n",
      "Time: 88.52 seconds\n",
      "Score: 0.7860\n",
      "Score: 0.7999\n",
      "Score: 0.7722\n",
      "Score: 0.7787\n",
      "Score: 0.7690\n",
      "Score: 0.7860\n",
      "Score: 0.7999\n",
      "Score: 0.7722\n",
      "Score: 0.7787\n",
      "Score: 0.7690\n"
     ]
    },
    {
     "data": {
      "image/png": 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S/wRw4kg6a2Zm5eWfXqkw51CsrJxD6b2y5lDMzMzackDpM56ntrLy2Kw+/wNb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d101893b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma      lambda  max_depth  min_child_weight\n",
      "0           0.072342  0.025001  152.850175        4.0         25.730014\n",
      "Score: 0.7895\n",
      "Time: 120.33 seconds\n",
      "Score: 0.8032\n",
      "Time: 117.20 seconds\n",
      "Score: 0.7743\n",
      "Time: 85.28 seconds\n",
      "Score: 0.7809\n",
      "Time: 84.19 seconds\n",
      "Score: 0.7704\n",
      "Time: 80.84 seconds\n",
      "Score: 0.7895\n",
      "Score: 0.8032\n",
      "Score: 0.7743\n",
      "Score: 0.7809\n",
      "Score: 0.7704\n",
      "Score: 0.7895\n",
      "Score: 0.8032\n",
      "Score: 0.7743\n",
      "Score: 0.7809\n",
      "Score: 0.7704\n"
     ]
    },
    {
     "data": {
      "image/png": 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DkxQEVtS1n0e+q7HBtr6dx9UvScd2Md/wwfwePEo6pj4FTCssvyA/v0dIt70fULftrwLH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\nBxQzM+sIBxQzM+uI/w/gwv6zwv0wBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d101eeff60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel    gamma    lambda  max_depth  min_child_weight\n",
      "0           0.053388  0.00616  3.133251        4.0          2.015256\n",
      "Score: 0.7830\n",
      "Time: 51.77 seconds\n",
      "Score: 0.8001\n",
      "Time: 83.49 seconds\n",
      "Score: 0.7695\n",
      "Time: 48.55 seconds\n",
      "Score: 0.7812\n",
      "Time: 74.30 seconds\n",
      "Score: 0.7687\n",
      "Time: 53.59 seconds\n",
      "Score: 0.7830\n",
      "Score: 0.8001\n",
      "Score: 0.7695\n",
      "Score: 0.7812\n",
      "Score: 0.7687\n",
      "Score: 0.7830\n",
      "Score: 0.8001\n",
      "Score: 0.7695\n",
      "Score: 0.7812\n",
      "Score: 0.7687\n"
     ]
    },
    {
     "data": {
      "image/png": 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/ODGQpw91pzlm27hv7l7ayqFImilptaQ1ki5oMP98Scsl3S5ppaQnJfXleedJ\nulPSHZI+K2mvPH2ypFsl3S3pFkmTOrtrZmY2nlrmUCRNANYAJwEbgGXA7IhY3aT+q4F3RMTJkg4F\nlgJHR8RvJH0O+HJEfFrSQuDnEXFJDlKTI2J+g/WFT1fMzEZpiFLmUE4E7omItQCSFgOzgIYBBZgD\nXFso7wE8XdJTwL7AA3n6LGBGfn016Yp3p4ACEAs8StpIuz/qGhoY2DassMP0GTMYanGrpn84ZmMx\nHn1zNNvpRRoa11gCtDfkdRiwrlBen6ftRNI+wEzgeoCI2AB8CLifFEg2R8TXcvWDImJjrvcQcNBY\ndsBGp9btBpg1Uet2A2yXdTopfyqwNCI2A+Q8yixgCvAI8AVJZ0TENQ2WbXqeMTg4yNSpUwHo6+uj\nv7+fgYEBYPuPoVxuXt6w334MzUgXg99ev55F++3H1L4+9p42reXyUKNWK9f+uFz+8nB6vVX9Dfvt\nx+DxxzO1r4/7Nm9mUV766GnT2lre/XN7uVarsWjRIoBt35fjrZ0cynRgKCJm5vJ8ICJiYYO6NwDX\nRcTiXH4dcEpEvDWX3wT8fkTMk7QKGIiIjZIOAZZExDEN1unfoTThZ3lZWflZXt1X1md5LQOOlDQl\n36E1G7ipvlK+S2sGcGNh8v3AdEl7SxIpsb8qz7sJGMyvz6pbzszMKqZlQImIrcA84FbgLmBxRKyS\nNFfS2YWqpwG3RMQThWW/C3wBWA58HxAw/GulhcArJd1NCjQXd2B/rIXtQxJm5eK+WX1t5VAi4mbg\nqLppn6grX026W6t+2YuAixpMfxg4eTSNNTOz8vKzvCrMORQrK+dQuq+sORQzM7OWHFB6jMeprazc\nN6vPAcXMzDrCOZQKcw7Fyso5lO5zDsXMzCrLAaXHeJzaysp9s/ocUMzMrCOcQ6kw51CsrJxD6T7n\nUMzMrLIcUHqMx6mtrNw3q88BxczMOsI5lApzDsXKyjmU7nMOxczMKssBpcd4nNrKyn2z+hxQzMys\nI5xDqTDnUKysnEPpPudQzMysshxQeozHqa2s3Derr62AImmmpNWS1ki6oMH88yUtl3S7pJWSnpTU\nJ2laYfpySY9IOicvs0DS+jzvdkkzO71zZmY2flrmUCRNANYAJwEbgGXA7IhY3aT+q4F3RMTJDdaz\nHjgxItZLWgA8FhGXtti+cyhNOIdiZeUcSveVNYdyInBPRKyNiC3AYmDWCPXnANc2mH4y8KOIWF+Y\nNq47a2ZVpYwzAAAHh0lEQVRmvz3tBJTDgHWF8vo8bSeS9gFmAtc3mP16dg408yStkHSVpElttMV2\nkceprazcN6tvYofXdyqwNCI2FydK2hN4DTC/MPkK4D0REZLeB1wKvKXRSgcHB5k6dSoAfX199Pf3\nMzAwAGzvhC63V16xYsWo6kONWq087Xe5GmUYn+25f24v12o1Fi1aBLDt+3K8tZNDmQ4MRcTMXJ4P\nREQsbFD3BuC6iFhcN/01wNuH19FguSnAlyLiuAbznENpwjkUKyvnULqvrDmUZcCRkqZI2guYDdxU\nXykPWc0Abmywjp3yKpIOKRRPB+5st9FmZlY+LQNKRGwF5gG3AncBiyNilaS5ks4uVD0NuCUinigu\nL2lfUkL+hrpVXyLpDkkrSIHovF3YD2vT9iEJs3Jx36y+tnIoEXEzcFTdtE/Ula8Grm6w7C+BZzWY\nfuaoWmpmZqXmZ3lVmHMoVlbOoXRfWXMoZmZmLTmg9BiPU1tZuW9WnwOKmZl1hHMoFeYcipWVcyjd\n5xyKmZlVlgNKj/E4tZWV+2b1OaCYmVlHOIdSYc6hWFk5h9J9zqGYmVllOaD0GI9TW1m5b1afA4qZ\nmXWEcygV5hyKlZVzKN3nHIqZmVWWA0qP8Ti1lZX7ZvU5oJiZWUc4h1JhzqFYWTmH0n3OoZiZWWU5\noPQYj1NbWblvVl9bAUXSTEmrJa2RdEGD+edLWi7pdkkrJT0pqU/StML05ZIekXROXmaypFsl3S3p\nFkmTOr1zZmY2flrmUCRNANYAJwEbgGXA7IhY3aT+q4F3RMTJDdazHjgxItZLWgj8PCIuyUFqckTM\nb7C+WLBkAUMDQztta6g2xEW3XbTT9AUzXN/1Xd/1e7z+EOOeQ2knoEwHFkTEH+XyfCAiYmGT+p8F\nvh4Rn6yb/irg3RHxslxeDcyIiI2SDgFqEXF0g/U5Kd+Ek/JWVk7Kd19Zk/KHAesK5fV52k4k7QPM\nBK5vMPv1wLWF8kERsREgIh4CDmqnwbZrPE5tZeW+WX0TO7y+U4GlEbG5OFHSnsBrgJ2GtAqanmcM\nDg4ydepUAPr6+ujv72dgYADY3gldbq+8YsWKUdWHGrVaedrvcjXKMD7bc//cXq7VaixatAhg2/fl\neGt3yGsoImbmctMhL0k3ANdFxOK66a8B3j68jjxtFTBQGPJaEhHHNFinh7ya8JCXlZWHvLqvrENe\ny4AjJU2RtBcwG7ipvlK+S2sGcGODdcxhx+Eu8joG8+uzmixnZmYV0TKgRMRWYB5wK3AXsDgiVkma\nK+nsQtXTgFsi4oni8pL2BU4Gbqhb9ULglZLuJt1BdvHYd6N3SaP9q42q/uTJ3d5Dq6rfdt90/ywf\nP3qlx0g1Iga63QyznbhvdlY3hrwcUHqMx5ytrNw3O6usORQzM7OWOn3bsJWANPJJSbPZvhK08TBS\n/3TfrDZfoeyGIqLp35IlS5rOMxsP7pu7L+dQzMx2Q86hmJlZZTmg9Jjtj8YwKxf3zepzQDEzs45w\nDsXMbDfkHIqZmVWWA0qP8Ti1lZX7ZvU5oJiZWUc4h2JmthtyDsXMzCrLAaXHeJzaysp9s/ocUMzM\nrCOcQzEz2w05h2JmZpXVVkCRNFPSaklrJF3QYP75kpZLul3SSklPSurL8yZJ+rykVZLukvT7efoC\nSevzMrdLmtnZXbNGPE5tZeW+WX0tA4qkCcBlwCnAscAcSUcX60TEP0TEiyLiBOBCoBYRm/PsjwL/\nERHHAMcDqwqLXhoRJ+S/mzuwP9bCihUrut0Es4bcN6uvnSuUE4F7ImJtRGwBFgOzRqg/B7gWQNIz\ngJdFxKcAIuLJiHi0UHdcx/cMNm/e3LqSWRe4b1ZfOwHlMGBdobw+T9uJpH2AmcD1edJzgZ9J+lQe\n1roy1xk2T9IKSVdJmjSG9puZWUl0Oil/KrC0MNw1ETgBuDwPh/0SmJ/nXQE8LyL6gYeASzvcFmvg\nvvvu63YTzBpy39wNjPT/j+fbdacDNxfK84ELmtS9AZhdKB8M3FsovxT4UoPlpgB3NFln+M9//vOf\n/0b/1+r7vdN/E2ltGXCkpCnAg8BsUp5kB3nIagbwhuFpEbFR0jpJ0yJiDXAS8INc/5CIeChXPR24\ns9HGx/s+ajMzG5uWASUitkqaB9xKGiL7ZESskjQ3zY4rc9XTgFsi4om6VZwDfFbSnsC9wJvz9Esk\n9QNPAfcBc3d5b8zMrGtK/0t5MzOrBv9SvkdI+qSkjZLu6HZbzIokHS7p6/mHzyslndPtNtnY+Aql\nR0h6KfA48OmIOK7b7TEbJukQ4JCIWCFpP+C/gVkRsbrLTbNR8hVKj4iIpcCmbrfDrF5EPBQRK/Lr\nx0lP02j4WzcrNwcUMysNSVOBfuA73W2JjYUDipmVQh7u+gJwbr5SsYpxQDGzrpM0kRRMPhMRN3a7\nPTY2Dii9RfiBnFZO/wL8ICI+2u2G2Ng5oPQISdcA3wSmSbpf0ptbLWM2HiS9hPSEjVcU/l8l//9I\nFeTbhs3MrCN8hWJmZh3hgGJmZh3hgGJmZh3hgGJmZh3hgGJmZh3hgGJmZh3hgGJmZh3hgGJmZh3x\n/wEhSVVPc37/DAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d119d3e898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma    lambda  max_depth  min_child_weight\n",
      "0           0.117038  0.007919  7.421553        4.0          4.159425\n",
      "Score: 0.7841\n",
      "Time: 75.52 seconds\n",
      "Score: 0.8000\n",
      "Time: 77.83 seconds\n",
      "Score: 0.7721\n",
      "Time: 56.68 seconds\n",
      "Score: 0.7797\n",
      "Time: 61.42 seconds\n",
      "Score: 0.7691\n",
      "Time: 68.25 seconds\n",
      "Score: 0.7841\n",
      "Score: 0.8000\n",
      "Score: 0.7721\n",
      "Score: 0.7797\n",
      "Score: 0.7691\n",
      "Score: 0.7841\n",
      "Score: 0.8000\n",
      "Score: 0.7721\n",
      "Score: 0.7797\n",
      "Score: 0.7691\n"
     ]
    },
    {
     "data": {
      "image/png": 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zM7OWHFB6zLbbOs3KxX2z+hxQzMysI5xDqTDnUKysnEPpPudQzMysshxQeozH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\nzKwjHFDMzKwjHFDMzKwj/hvLjcwFGAV4uAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d101f79898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma    lambda  max_depth  min_child_weight\n",
      "0           0.126068  0.014513  1.223254        4.0          0.149984\n",
      "Score: 0.7841\n",
      "Time: 66.29 seconds\n",
      "Score: 0.7977\n",
      "Time: 59.25 seconds\n",
      "Score: 0.7697\n",
      "Time: 44.77 seconds\n",
      "Score: 0.7729\n",
      "Time: 55.64 seconds\n",
      "Score: 0.7673\n",
      "Time: 52.14 seconds\n",
      "Score: 0.7841\n",
      "Score: 0.7977\n",
      "Score: 0.7697\n",
      "Score: 0.7729\n",
      "Score: 0.7673\n",
      "Score: 0.7841\n",
      "Score: 0.7977\n",
      "Score: 0.7697\n",
      "Score: 0.7729\n",
      "Score: 0.7673\n"
     ]
    },
    {
     "data": {
      "image/png": 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DuKEw+UFguqQ9JImU2F+R590IzM3vz6xbzszMKqZlQImIrcDZwK3APcCSiFgh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\nrCMcUMzMrCMcUMzMrCMcUMzMrCP+GyaSvUTj9DOgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d101707e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma     lambda  max_depth  min_child_weight\n",
      "0           0.066398  6.441836  64.979154        3.0         24.571331\n",
      "Score: 0.7832\n",
      "Time: 86.72 seconds\n",
      "Score: 0.7966\n",
      "Time: 135.13 seconds\n",
      "Score: 0.7741\n",
      "Time: 121.37 seconds\n",
      "Score: 0.7794\n",
      "Time: 169.59 seconds\n",
      "Score: 0.7665\n",
      "Time: 127.90 seconds\n",
      "Score: 0.7832\n",
      "Score: 0.7966\n",
      "Score: 0.7741\n",
      "Score: 0.7794\n",
      "Score: 0.7665\n",
      "Score: 0.7832\n",
      "Score: 0.7966\n",
      "Score: 0.7741\n",
      "Score: 0.7794\n",
      "Score: 0.7665\n"
     ]
    },
    {
     "data": {
      "image/png": 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s2ia4fTbRiduGHVAqzL9DsbLy71A6z8/yMjOzynJA6TI7b+s0Kxe3zepzQDEz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\n4YBiZmZt8f8BDSIesLxawlgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d1000efcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma    lambda  max_depth  min_child_weight\n",
      "0           0.059174  2.841783  2.733476        4.0          0.351166\n",
      "Score: 0.7835\n",
      "Time: 43.56 seconds\n",
      "Score: 0.7960\n",
      "Time: 67.81 seconds\n",
      "Score: 0.7706\n",
      "Time: 42.35 seconds\n",
      "Score: 0.7779\n",
      "Time: 44.66 seconds\n",
      "Score: 0.7685\n",
      "Time: 38.65 seconds\n",
      "Score: 0.7835\n",
      "Score: 0.7960\n",
      "Score: 0.7706\n",
      "Score: 0.7779\n",
      "Score: 0.7685\n",
      "Score: 0.7835\n",
      "Score: 0.7960\n",
      "Score: 0.7706\n",
      "Score: 0.7779\n",
      "Score: 0.7685\n"
     ]
    },
    {
     "data": {
      "image/png": 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16kXZ1n6zciUDt90GQI36ZAQMdKc7Zpt5bI4tbeVQIuJm4JCGZZc1lBey5a6t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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d1001245c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel    gamma      lambda  max_depth  min_child_weight\n",
      "0           0.081782  0.37786  218.150352        3.0          2.177725\n",
      "Score: 0.7859\n",
      "Time: 110.83 seconds\n",
      "Score: 0.8024\n",
      "Time: 130.54 seconds\n",
      "Score: 0.7762\n",
      "Time: 81.85 seconds\n",
      "Score: 0.7799\n",
      "Time: 89.65 seconds\n",
      "Score: 0.7693\n",
      "Time: 83.10 seconds\n",
      "Score: 0.7859\n",
      "Score: 0.8024\n",
      "Score: 0.7762\n",
      "Score: 0.7799\n",
      "Score: 0.7693\n",
      "Score: 0.7859\n",
      "Score: 0.8024\n",
      "Score: 0.7762\n",
      "Score: 0.7799\n",
      "Score: 0.7693\n"
     ]
    },
    {
     "data": {
      "image/png": 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PknSbpPsk3SppYnt3zczMRlPTHIqkCcAK4CRgDbAYmB4Ryxu0PxU4PyJOlrQ/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\n8AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d10a0447f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma     lambda  max_depth  min_child_weight\n",
      "0           0.146283  0.023728  46.961479        3.0          1.055752\n",
      "Score: 0.7871\n",
      "Time: 102.65 seconds\n",
      "Score: 0.8003\n",
      "Time: 105.85 seconds\n",
      "Score: 0.7744\n",
      "Time: 65.03 seconds\n",
      "Score: 0.7811\n",
      "Time: 92.79 seconds\n",
      "Score: 0.7721\n",
      "Time: 65.11 seconds\n",
      "Score: 0.7871\n",
      "Score: 0.8003\n",
      "Score: 0.7744\n",
      "Score: 0.7811\n",
      "Score: 0.7721\n",
      "Score: 0.7871\n",
      "Score: 0.8003\n",
      "Score: 0.7744\n",
      "Score: 0.7811\n",
      "Score: 0.7721\n"
     ]
    },
    {
     "data": {
      "image/png": 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/ZGG5psd+q8dB/d9gAnxIkqaTAsE44PMRMb9u/otI+YTDSIHgExGxYKhlJc0jfakO5kj+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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d1010cc2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma     lambda  max_depth  min_child_weight\n",
      "0           0.042415  0.041511  15.241821        3.0         11.243902\n",
      "Score: 0.7865\n",
      "Time: 59.91 seconds\n",
      "Score: 0.8009\n",
      "Time: 72.29 seconds\n",
      "Score: 0.7712\n",
      "Time: 44.01 seconds\n",
      "Score: 0.7810\n",
      "Time: 64.87 seconds\n",
      "Score: 0.7716\n",
      "Time: 57.21 seconds\n",
      "Score: 0.7865\n",
      "Score: 0.8009\n",
      "Score: 0.7712\n",
      "Score: 0.7810\n",
      "Score: 0.7716\n",
      "Score: 0.7865\n",
      "Score: 0.8009\n",
      "Score: 0.7712\n",
      "Score: 0.7810\n",
      "Score: 0.7716\n"
     ]
    },
    {
     "data": {
      "image/png": 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88J0cXqhzOOlKZX3+TF9TmHcZ8NERtuvlpP3j19TdwUnKHd1ZV/9s8l2NDdZ1S27Dr0j7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\nqskAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d1018062e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma      lambda  max_depth  min_child_weight\n",
      "0            0.07396  0.478429  178.601985        2.0          1.262995\n",
      "Score: 0.7888\n",
      "Time: 151.37 seconds\n",
      "Score: 0.8035\n",
      "Time: 169.20 seconds\n",
      "Score: 0.7728\n",
      "Time: 81.98 seconds\n",
      "Score: 0.7805\n",
      "Time: 108.24 seconds\n",
      "Score: 0.7678\n",
      "Time: 98.21 seconds\n",
      "Score: 0.7888\n",
      "Score: 0.8035\n",
      "Score: 0.7728\n",
      "Score: 0.7805\n",
      "Score: 0.7678\n",
      "Score: 0.7888\n",
      "Score: 0.8035\n",
      "Score: 0.7728\n",
      "Score: 0.7805\n",
      "Score: 0.7678\n"
     ]
    },
    {
     "data": {
      "image/png": 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L6db04YAyi3SC9xjpZP8jhfWaHvu0eRzU/w0nwEckaQYpEEwAPhkR9fPezyblEw4nBYIP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\nA5oxvyp77AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d1021df940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma     lambda  max_depth  min_child_weight\n",
      "0           0.090157  0.292144  45.546531        3.0          0.517258\n",
      "Score: 0.7875\n",
      "Time: 92.46 seconds\n",
      "Score: 0.8022\n",
      "Time: 93.64 seconds\n",
      "Score: 0.7746\n",
      "Time: 75.91 seconds\n",
      "Score: 0.7807\n",
      "Time: 79.61 seconds\n",
      "Score: 0.7714\n",
      "Time: 56.56 seconds\n",
      "Score: 0.7875\n",
      "Score: 0.8022\n",
      "Score: 0.7746\n",
      "Score: 0.7807\n",
      "Score: 0.7714\n",
      "Score: 0.7875\n",
      "Score: 0.8022\n",
      "Score: 0.7746\n",
      "Score: 0.7807\n",
      "Score: 0.7714\n"
     ]
    },
    {
     "data": {
      "image/png": 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2q89DXhXWbnf/4rPO4perVwPw4MaNTO7rA2DPKVOYf8UVQ63qIQUbkdFom8N5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\nFWfuGGMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d10a0492b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma      lambda  max_depth  min_child_weight\n",
      "0           0.087899  0.051213  122.864554        3.0          0.463737\n",
      "Score: 0.7871\n",
      "Time: 89.97 seconds\n",
      "Score: 0.8021\n",
      "Time: 118.49 seconds\n",
      "Score: 0.7746\n",
      "Time: 71.60 seconds\n",
      "Score: 0.7812\n",
      "Time: 69.81 seconds\n",
      "Score: 0.7706\n",
      "Time: 74.21 seconds\n",
      "Score: 0.7871\n",
      "Score: 0.8021\n",
      "Score: 0.7746\n",
      "Score: 0.7812\n",
      "Score: 0.7706\n",
      "Score: 0.7871\n",
      "Score: 0.8021\n",
      "Score: 0.7746\n",
      "Score: 0.7812\n",
      "Score: 0.7706\n"
     ]
    },
    {
     "data": {
      "image/png": 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bCf167VoWffvbI8oXTXxTzJ7BfXPn4iGvHlPrdgPMmqh1uwG2wyox5OXTlQ66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\nM7OOcEAxM7OOcEAxM7OOcEAxM7OO+P/n0iMpG3AziwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d1000131d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel    gamma     lambda  max_depth  min_child_weight\n",
      "0           0.071358  0.20745  35.745627        2.0          1.586639\n",
      "Score: 0.7877\n",
      "Time: 129.87 seconds\n",
      "Score: 0.8029\n",
      "Time: 149.66 seconds\n",
      "Score: 0.7730\n",
      "Time: 81.58 seconds\n",
      "Score: 0.7806\n",
      "Time: 103.01 seconds\n",
      "Score: 0.7698\n",
      "Time: 78.14 seconds\n",
      "Score: 0.7877\n",
      "Score: 0.8029\n",
      "Score: 0.7730\n",
      "Score: 0.7806\n",
      "Score: 0.7698\n",
      "Score: 0.7877\n",
      "Score: 0.8029\n",
      "Score: 0.7730\n",
      "Score: 0.7806\n",
      "Score: 0.7698\n"
     ]
    },
    {
     "data": {
      "image/png": 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VhOT8vr5t0wrPqJ82jflNbtV00tOGYyTG5lCeZyzyFxttp6t0ugNmDVQ63QHb\nYU7KjwG7H374tlnD+zdtotLTs63erJM8NkcXT3l1MX8PxcrK30PpPE95mZlZ13JAGWP8e0lWVh6b\n3c8BxczM2sI5lC7mHIqVlXMoneccipmZdS0HlDHG89RWVh6b3a+lgCJpuqRVklZLuqDO8vMlLZN0\nu6QVkrZI6snL3iXpLkl3SvqCpN1y/URJt0m6R9Ktkia0d9fMzGwkNc2hSBoHrAZOAtYDS4GZEbGq\nQfvXAO+MiJMlHQwsAY6MiD9K+iLw9Yi4RtIC4JGIuCQHqYkRMbfO9pxDaUQjND3q19+GaqTGJnh8\nNlDWHMoJwL0RsSYiNgOLgRmDtJ8FXFco7wI8W9J4YE/gwVw/A1iU/14EnD6UjhuISAfTTnwIH6w2\ndCMxNj0+y6eVgDIJWFsor8t125G0BzAduAEgItYDHwMeIAWSTRHxzdz8gIjYkNs9DBwwnB2wofE8\ntZWVx2b3a/dveZ0KLImITQA5jzIDmAw8BnxZ0pkRcW2ddRueavT39zNlyhQAenp66O3tpa+vDxgY\nhC63Vl6+fPmQ2kOFSqU8/Xe5O8rV/yprZz+fx+dAuVKpsHDhQoBtn5cjrZUcylRgfkRMz+W5QETE\ngjptbwSuj4jFufxa4JSIeEsuvwF4SUTMkbQS6IuIDZIOAr4dEUfV2aZzKA34eyhWVv4eSueVNYey\nFDhM0uR8h9ZM4ObaRvkurWnATYXqB4CpknaXJFJif2VedjPQn/9+Y816ZmbWZZoGlIjYCswBbgPu\nBhZHxEpJsyWdXWh6OnBrRDxVWPfHwJeBZcAdgIAr8+IFwCsl3UMKNBe3YX+siYEpCbNy8djsfi3l\nUCLiFuCImrorasqLGLhrq1h/IXBhnfqNwMlD6ayZmZWXf8urizmHYmXlHErnlTWHYmZm1pQDyhjj\neWorK4/N7ueAYmZmbeEcShdzDsXKyjmUznMOxczMupYDyhjjeWorK4/N7ueAYmZmbeEcShdzDsXK\nyjmUznMOxczMupYDyhjjeWorK4/N7ueAYmZmbeEcShdzDsXKyjmUznMOxczMupYDyhjjeWorK4/N\n7ueAYmZmbeEcShdzDsXKyjmUznMOxczMupYDyhjjeWorK4/N7tdSQJE0XdIqSaslXVBn+fmSlkm6\nXdIKSVsk9Ug6vFC/TNJjks7N68yTtC4vu13S9HbvnJmZjZymORRJ44DVwEnAemApMDMiVjVo/xrg\nnRFxcp3wX2K/AAAH0klEQVTtrANOiIh1kuYBT0TEpU2e3zmUBpxDsbJyDqXzyppDOQG4NyLWRMRm\nYDEwY5D2s4Dr6tSfDPwiItYV6kZ0Z83MbOdpJaBMAtYWyuty3XYk7QFMB26os/h1bB9o5khaLukq\nSRNa6IvtIM9TW1l5bHa/8W3e3qnAkojYVKyUtCtwGjC3UH058MGICEkfAi4F3lxvo/39/UyZMgWA\nnp4eent76evrAwYGocutlZcvXz6k9lChUilP/13ujjKMzPN5fA6UK5UKCxcuBNj2eTnSWsmhTAXm\nR8T0XJ4LREQsqNP2RuD6iFhcU38acE51G3XWmwx8NSKOrbPMOZQGnEOxsnIOpfPKmkNZChwmabKk\n3YCZwM21jfKU1TTgpjrb2C6vIumgQvEM4K5WO21mZuXTNKBExFZgDnAbcDewOCJWSpot6exC09OB\nWyPiqeL6kvYkJeRvrNn0JZLulLScFIjetQP7YS0amJIwKxePze7XUg4lIm4Bjqipu6KmvAhYVGfd\n3wH716k/a0g9NTOzUvNveXUx51CsrJxD6byy5lDMzMyackAZYzxPbWXlsdn9HFDMzKwtnEPpYs6h\nWFk5h9J5zqGYmVnXckDpctJQH5UhtZ84sdN7aN1qZ49Nj8/yafdvedkIGs6lvqcIbCR4bI5NzqGM\nMT5oraw8NtvLOZQG5lfmN6zXhdru4faN2zO/XP1xe7cvjs0y9afb23eCr1DGGKlCRF+nu2G2HY/N\n9vIVipmZdS0HlDFm3ry+TnfBrC6Pze7nKS8zs1HIU1620/n3kqysPDa7nwOKmZm1hae8zMxGIU95\nmZlZ13JAGWP6+yud7oJZXR6b3a+lgCJpuqRVklZLuqDO8vMlLZN0u6QVkrZI6pF0eKF+maTHJJ2b\n15ko6TZJ90i6VdKEdu+cbW/Rok73wKw+j83u1zSHImkcsBo4CVgPLAVmRsSqBu1fA7wzIk6us511\nwAkRsU7SAuCRiLgkB6mJETG3zvacQ2kj+feSrKQ8NturrDmUE4B7I2JNRGwGFgMzBmk/C7iuTv3J\nwC8iYl0uzwCq5ySLgNNb67KZmZVRKwFlErC2UF6X67YjaQ9gOnBDncWv45mB5oCI2AAQEQ8DB7TS\nYdtRlU53wKyBSqc7YDuo3f8fyqnAkojYVKyUtCtwGrDdlFZBw4vd/v5+pkyZAkBPTw+9vb309fUB\nA1+Gcrm1MiynUilPf1x22eX2lCuVCgsXLgTY9nk50lrJoUwF5kfE9FyeC0RELKjT9kbg+ohYXFN/\nGnBOdRu5biXQFxEbJB0EfDsijqqzTedQhkga3rSpX2cbCcMZnx6bQ1fWHMpS4DBJkyXtBswEbq5t\nlO/SmgbcVGcb9fIqNwP9+e83NljPhiEihvUwGwkem6NX04ASEVuBOcBtwN3A4ohYKWm2pLMLTU8H\nbo2Ip4rrS9qTlJC/sWbTC4BXSrqHdAfZxcPfDWtV9RLZrGw8NrtfSzmUiLgFOKKm7oqa8iIG7toq\n1v8O2L9O/UZSoDEzs1HAv+VlZjYKlTWHYmZm1pQDyhjjeWorK4/N7ueAYmZmbeEcipnZKOQcipmZ\ndS0HlDHG89RWVh6b3c8BxczM2sI5FDOzUcg5FDMz61oOKGOM56mtrDw2u58DipmZtYVzKGZmo5Bz\nKGZm1rUcUMYYz1NbWXlsdj8HFDMzawvnUMzMRiHnUMzMrGu1FFAkTZe0StJqSRfUWX6+pGWSbpe0\nQtIWST152QRJX5K0UtLdkl6S6+dJWpfXuV3S9PbumtXjeWorK4/N7tc0oEgaB3wSOAU4Gpgl6chi\nm4j4vxFxfES8GHgvUImITXnxvwD/GRFHAccBKwurXhoRL86PW9qwP9bE8uXLO90Fs7o8NrtfK1co\nJwD3RsSaiNgMLAZmDNJ+FnAdgKR9gJdHxNUAEbElIh4vtB3R+T2DTZs2NW9k1gEem92vlYAyCVhb\nKK/LdduRtAcwHbghVz0P+I2kq/O01pW5TdUcScslXSVpwjD6b2ZmJdHupPypwJLCdNd44MXAZXk6\n7HfA3LzscuD5EdELPAxc2ua+WB33339/p7tgVpfH5igQEYM+gKnALYXyXOCCBm1vBGYWygcC9xXK\nJwJfrbPeZODOBtsMP/zwww8/hv5o9vne7sd4mlsKHCZpMvAQMJOUJ3mGPGU1DXh9tS4iNkhaK+nw\niFgNnAT8LLc/KCIezk3PAO6q9+QjfR+1mZkNT9OAEhFbJc0BbiNNkX0mIlZKmp0Wx5W56enArRHx\nVM0mzgW+IGlX4D7gTbn+Ekm9wNPA/cDsHd4bMzPrmNJ/U97MzLqDvyk/Rkj6jKQNku7sdF/MiiQd\nIulb+YvPKySd2+k+2fD4CmWMkHQi8CRwTUQc2+n+mFVJOgg4KCKWS9oL+CkwIyJWdbhrNkS+Qhkj\nImIJ8Gin+2FWKyIejojl+e8nSb+mUfe7blZuDihmVhqSpgC9wI862xMbDgcUMyuFPN31ZeC8fKVi\nXcYBxcw6TtJ4UjD5XETc1On+2PA4oIwtwj/IaeX0WeBnEfEvne6IDZ8Dyhgh6VrgB8Dhkh6Q9KZm\n65iNBEkvI/3Cxl8V/l8l//9IXci3DZuZWVv4CsXMzNrCAcXMzNrCAcXMzNrCAcXMzNrCAcXMzNrC\nAcXMzNrCAcXMzNrCAcXMzNri/wMnGdE1UIL4lAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d1021d72e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma     lambda  max_depth  min_child_weight\n",
      "0           0.041898  0.031186  17.014364        3.0          0.214541\n",
      "Score: 0.7869\n",
      "Time: 71.66 seconds\n",
      "Score: 0.8029\n",
      "Time: 85.67 seconds\n",
      "Score: 0.7731\n",
      "Time: 52.50 seconds\n",
      "Score: 0.7792\n",
      "Time: 53.06 seconds\n",
      "Score: 0.7701\n",
      "Time: 43.86 seconds\n",
      "Score: 0.7869\n",
      "Score: 0.8029\n",
      "Score: 0.7731\n",
      "Score: 0.7792\n",
      "Score: 0.7701\n",
      "Score: 0.7869\n",
      "Score: 0.8029\n",
      "Score: 0.7731\n",
      "Score: 0.7792\n",
      "Score: 0.7701\n"
     ]
    },
    {
     "data": {
      "image/png": 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ji848k1+vXr1D+d5HHsm8yy9vsMbI92FWNBZtcyT76UXdyKF0+mnDVkK/Xr2a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\n2yWeHuYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d101eac1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma      lambda  max_depth  min_child_weight\n",
      "0           0.094337  0.061709  399.313684        3.0         45.755995\n",
      "Score: 0.7875\n",
      "Time: 128.80 seconds\n",
      "Score: 0.8006\n",
      "Time: 147.85 seconds\n",
      "Score: 0.7728\n",
      "Time: 81.24 seconds\n",
      "Score: 0.7792\n",
      "Time: 105.38 seconds\n",
      "Score: 0.7670\n",
      "Time: 89.95 seconds\n",
      "Score: 0.7875\n",
      "Score: 0.8006\n",
      "Score: 0.7728\n",
      "Score: 0.7792\n",
      "Score: 0.7670\n",
      "Score: 0.7875\n",
      "Score: 0.8006\n",
      "Score: 0.7728\n",
      "Score: 0.7792\n",
      "Score: 0.7670\n"
     ]
    },
    {
     "data": {
      "image/png": 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SecC7gBeAJcCZEfFbSROBrwCTgAeBt0XEU03e20NeLYzlcr9WqzEwMLBF38Ns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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d119c85a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma     lambda  max_depth  min_child_weight\n",
      "0           0.079391  0.284368  83.187169        2.0          0.537734\n",
      "Score: 0.7880\n",
      "Time: 130.80 seconds\n",
      "Score: 0.8037\n",
      "Time: 158.08 seconds\n",
      "Score: 0.7744\n",
      "Time: 95.33 seconds\n",
      "Score: 0.7806\n",
      "Time: 88.02 seconds\n",
      "Score: 0.7685\n",
      "Time: 81.81 seconds\n",
      "Score: 0.7880\n",
      "Score: 0.8037\n",
      "Score: 0.7744\n",
      "Score: 0.7806\n",
      "Score: 0.7685\n",
      "Score: 0.7880\n",
      "Score: 0.8037\n",
      "Score: 0.7744\n",
      "Score: 0.7806\n",
      "Score: 0.7685\n"
     ]
    },
    {
     "data": {
      "image/png": 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PvjnW7fQLD3mZmVll+QqlwvwsLysrP8ur93yFYmZmleWA0mf8vCQrK/fN6nNA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\nStDhHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d119c8c2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma     lambda  max_depth  min_child_weight\n",
      "0           0.098315  0.094058  186.21266        3.0          2.901279\n",
      "Score: 0.7888\n",
      "Time: 105.25 seconds\n",
      "Score: 0.8032\n",
      "Time: 117.54 seconds\n",
      "Score: 0.7759\n",
      "Time: 88.19 seconds\n",
      "Score: 0.7821\n",
      "Time: 104.83 seconds\n",
      "Score: 0.7690\n",
      "Time: 83.60 seconds\n",
      "Score: 0.7888\n",
      "Score: 0.8032\n",
      "Score: 0.7759\n",
      "Score: 0.7821\n",
      "Score: 0.7690\n",
      "Score: 0.7888\n",
      "Score: 0.8032\n",
      "Score: 0.7759\n",
      "Score: 0.7821\n",
      "Score: 0.7690\n"
     ]
    },
    {
     "data": {
      "image/png": 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ennWYjUfbHOt6eoW7vMzMrLJ8hVJhfpaXlZWf5dV9vkIxM7PKckDpMX5ekpWV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\nEQ4oZmbWEf8fDU6QvCxW8ZwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d101f6ab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma      lambda  max_depth  min_child_weight\n",
      "0           0.096808  0.070813  272.806559        2.0         51.585159\n",
      "Score: 0.7865\n",
      "Time: 173.37 seconds\n",
      "Score: 0.8022\n",
      "Time: 198.34 seconds\n",
      "Score: 0.7727\n",
      "Time: 116.82 seconds\n",
      "Score: 0.7803\n",
      "Time: 136.30 seconds\n",
      "Score: 0.7684\n",
      "Time: 121.23 seconds\n",
      "Score: 0.7865\n",
      "Score: 0.8022\n",
      "Score: 0.7727\n",
      "Score: 0.7803\n",
      "Score: 0.7684\n",
      "Score: 0.7865\n",
      "Score: 0.8022\n",
      "Score: 0.7727\n",
      "Score: 0.7803\n",
      "Score: 0.7684\n"
     ]
    },
    {
     "data": {
      "image/png": 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S7pT0eUm75frJkm6VdI+kWyRNau+umZnZWGqaQ5E0AVgNnAysB5YBMyNiVYP2\nbwTeFRGnSDoQWAocERG/lvQF4KsRcbWkhcCjEXFpDlKTI2Jene05h9KAcyhWVs6hdF5ZcyjHAfdG\nxJqI2AQsAWYM034WcG2hvAvwfEkTgT2Bh3L9DGBxfrwYOH0kHTcIlI6o5/AvGNPxaOPEWIxNj8/y\naSWgHASsLZTX5bodSNoDGACuB4iI9cBHgQdJgWRjRPxnbr5fRGzI7R4B9hvNDvQyEenr2Qj+Krfd\nNqL2wl//bOTGYmx6fJZPu3+HchqwNCI2AuQ8ygxgCvAE8CVJZ0bENXXWbTgyBgcHmTp1KgB9fX30\n9/czffp0YFsiz+XWykNDQyNqDxUqlfL03+XuKFdv8/hcP5/H57ZypVJh0aJFAFs/L8daKzmU44EF\nETGQy/OAiIiFddreAFwXEUty+c3AqRHxjlx+G/CqiJgraSUwPSI2SDoAuC0ijqyzTedQGnAOxcrK\nOZTOK2sOZRlwqKQp+QqtmcBNtY3yVVrTgBsL1Q8Cx0vaXZJIif2VedlNwGB+PLtmPTMz6zJNA0pE\nbAHmArcCdwNLImKlpDmSzik0PR24JSKeKaz7A+BLwHLgDkBA9X/NWQi8VtI9pEBzSRv2x5rYNiVh\nVi4em92vpRxKRNwMHF5T9+ma8mK2XbVVrL8YuLhO/WPAKSPprJmZlZfv5dXFnEOxsnIOpfPKmkMx\nMzNrygGlx3ie2srKY7P7OaCYmVlbOIfSxZxDsbJyDqXznEMxM7Ou5YDSYzxPbWXlsdn9HFDMzKwt\nnEPpYs6hWFk5h9J5zqGYmVnXckDpMZ6ntrLy2Ox+DihmZtYWzqF0MedQrKycQ+k851DMzKxrOaD0\nGM9TW1l5bHY/BxQzM2sL51C6mHMoVlbOoXSecyhmZta1HFB6jOepraw8NrtfSwFF0oCkVZJWS7qw\nzvILJC2XdLukFZI2S+qTdFihfrmkJySdm9eZL2ldXna7pIF275yZmY2dpjkUSROA1cDJwHpgGTAz\nIlY1aP9G4F0RcUqd7awDjouIdZLmA09FxGVNnt85lAacQ7Gycg6l88qaQzkOuDci1kTEJmAJMGOY\n9rOAa+vUnwL8JCLWFerGdGfNzOy500pAOQhYWyivy3U7kLQHMABcX2fxW9gx0MyVNCTpKkmTWuiL\n7STPU1tZeWx2v4lt3t5pwNKI2FislLQr8CZgXqH6CuADERGSPgRcBry93kYHBweZOnUqAH19ffT3\n9zN9+nRLP0krAAAHMElEQVRg2yB0ubXy0NDQiNpDhUqlPP13uTvKMDbP5/G5rVypVFi0aBHA1s/L\nsdZKDuV4YEFEDOTyPCAiYmGdtjcA10XEkpr6NwF/Ud1GnfWmAF+JiKPrLHMOpQHnUKysnEPpvLLm\nUJYBh0qaImk3YCZwU22jPGU1DbixzjZ2yKtIOqBQPAO4q9VOm5lZ+TQNKBGxBZgL3ArcDSyJiJWS\n5kg6p9D0dOCWiHimuL6kPUkJ+RtqNn2ppDslDZEC0fk7sR/Wom1TEmbl4rHZ/VrKoUTEzcDhNXWf\nrikvBhbXWfcXwIvq1J81op6amVmp+V5eXcw5FCsr51A6r6w5FDMzs6YcUHqM56mtrDw2u58DipmZ\ntYVzKF3MORQrK+dQOs85FDMz61oOKD3G89RWVh6b3c8BxczM2sI5lC6mMZgdnTwZHnvsuX8eG1/G\nYmyCx+dwOpFDaffdhm0MjSbOOolpY8Fjszd5yqvnVDrdAbMGKp3ugO0kBxQzM2sL51B6jKcVrKw8\nNtvLv0MxM7Ou1RUBZUFlQcN6Xawd/ty+cXtml6s/bu/2xbFZpv50e/tO8JRXj6lUKoX/j9usPDw2\n26sTU14OKGZm45BzKGZm1rUcUHqM75dkZeWx2f1aCiiSBiStkrRa0oV1ll8gabmk2yWtkLRZUp+k\nwwr1yyU9IencvM5kSbdKukfSLZImtXvnzMxs7DQNKJImAJ8ETgWOAmZJOqLYJiL+LiKOiYhjgYuA\nSkRsjIjVhfrfBn4O3JBXmwd8PSIOB76R17PnWKUyvdNdMKvLY7P7NU3KSzoemB8Rr8/leUBExMIG\n7T8PfCMiPlNT/zrgfRFxYi6vAqZFxAZJB5CC0BF1tuekfBvJPx6zkvLYbK+yJuUPAtYWyuty3Q4k\n7QEMANfXWfwW4NpCeb+I2AAQEY8A+7XSYdtZlU53wKyBSqc7YDup3XcbPg1YGhEbi5WSdgXeRJrm\naqThd5PBwUGmTp0KQF9fH/39/VuvV68m8lxurQxDVCrl6Y/LLrvcnnKlUmHRokUAWz8vx1qrU14L\nImIglxtOeUm6AbguIpbU1L8J+IvqNnLdSmB6Ycrrtog4ss42PeXVRp5WsLLy2Gyvsk55LQMOlTRF\n0m7ATOCm2kb5Kq1pwI11tjGL7ae7yNsYzI9nN1jPzMy6RNOAEhFbgLnArcDdwJKIWClpjqRzCk1P\nB26JiGeK60vaEziFbVd3VS0EXivpHuBk4JLR74a1avbsSqe7YFaXx2b3861XekzF90uykvLYbC/f\ny6sOBxQzs5Eraw7FzMysKQeUHlO9zNCsbDw2u58DipmZtUW7f9hoJSCNbtrUuSobC6MZnx6b3cEB\nZRzywWdl5vE5fnnKq8d4ntrKymOz+zmgmJlZW/h3KGZm45B/h2JmZl3LAaXHeJ7ayspjs/s5oJiZ\nWVs4h2JmNg45h2JmZl3LAaXHeJ7ayspjs/s5oJiZWVs4h2JmNg45h2JmZl2rpYAiaUDSKkmrJV1Y\nZ/kFkpZLul3SCkmbJfXlZZMkfVHSSkl3S3pVrp8vaV1e53ZJA+3dNavH89RWVh6b3a9pQJE0Afgk\ncCpwFDBL0hHFNhHxdxFxTEQcC1wEVCJiY178CeDfI+JI4JXAysKql0XEsfnv5jbsjzUxNDTU6S6Y\n1eWx2f1aOUM5Drg3ItZExCZgCTBjmPazgGsBJO0NnBgRnwWIiM0R8WSh7ZjO7xls3LixeSOzDvDY\n7H6tBJSDgLWF8rpctwNJewADwPW56qXAzyR9Nk9rXZnbVM2VNCTpKkmTRtF/MzMriXYn5U8Dlham\nuyYCxwKX5+mwXwDz8rIrgJdFRD/wCHBZm/tidTzwwAOd7oJZXR6b40BEDPsHHA/cXCjPAy5s0PYG\nYGahvD9wX6F8AvCVOutNAe5ssM3wn//85z//jfyv2ed7u/9a+S+AlwGHSpoCPAzMJOVJtpOnrKYB\nb63WRcQGSWslHRYRq4GTgR/l9gdExCO56RnAXfWefKyvozYzs9FpGlAiYoukucCtpCmyz0TESklz\n0uK4Mjc9HbglIp6p2cS5wOcl7QrcB5yd6y+V1A88CzwAzNnpvTEzs44p/S/lzcysO/iX8j1C0mck\nbZB0Z6f7YlYk6WBJ38g/fF4h6dxO98lGx2coPULSCcDTwNURcXSn+2NWJekA4ICIGJL0AuC/gRkR\nsarDXbMR8hlKj4iIpcDjne6HWa2IeCQihvLjp0l306j7WzcrNwcUMysNSVOBfuD7ne2JjYYDipmV\nQp7u+hJwXj5TsS7jgGJmHSdpIimYfC4ibux0f2x0HFB6i/ANOa2c/hn4UUR8otMdsdFzQOkRkq4B\nvgscJulBSWc3W8dsLEh6DekOG79X+H+V/P8jdSFfNmxmZm3hMxQzM2sLBxQzM2sLBxQzM2sLBxQz\nM2sLBxQzM2sLBxQzM2sLBxQzM2sLBxQzM2uL/w/CokJED4kzPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d101707d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma      lambda  max_depth  min_child_weight\n",
      "0           0.097941  0.016033  159.961967        3.0         31.290382\n",
      "Score: 0.7896\n",
      "Time: 110.97 seconds\n",
      "Score: 0.8044\n",
      "Time: 136.29 seconds\n",
      "Score: 0.7742\n",
      "Time: 65.79 seconds\n",
      "Score: 0.7805\n",
      "Time: 83.05 seconds\n",
      "Score: 0.7710\n",
      "Time: 78.77 seconds\n",
      "Score: 0.7896\n",
      "Score: 0.8044\n",
      "Score: 0.7742\n",
      "Score: 0.7805\n",
      "Score: 0.7710\n",
      "Score: 0.7896\n",
      "Score: 0.8044\n",
      "Score: 0.7742\n",
      "Score: 0.7805\n",
      "Score: 0.7710\n"
     ]
    },
    {
     "data": {
      "image/png": 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OnOimmG3BfXP74iGvChvP5X6tVis8tuLZ2YbZRPTN8W6nW3jIy8zMKstXKBXm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\nH3+VqluLnuHqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d10a0676d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma      lambda  max_depth  min_child_weight\n",
      "0            0.13111  0.012193  347.392483        3.0         11.338068\n",
      "Score: 0.7887\n",
      "Time: 130.25 seconds\n",
      "Score: 0.8042\n",
      "Time: 185.11 seconds\n",
      "Score: 0.7743\n",
      "Time: 96.49 seconds\n",
      "Score: 0.7810\n",
      "Time: 136.51 seconds\n",
      "Score: 0.7706\n",
      "Time: 131.27 seconds\n",
      "Score: 0.7887\n",
      "Score: 0.8042\n",
      "Score: 0.7743\n",
      "Score: 0.7810\n",
      "Score: 0.7706\n",
      "Score: 0.7887\n",
      "Score: 0.8042\n",
      "Score: 0.7743\n",
      "Score: 0.7810\n",
      "Score: 0.7706\n"
     ]
    },
    {
     "data": {
      "image/png": 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Osa6/pm5cHzg/t+NXwCfrpm2xPtIxuQ7obbL+GTT4giTlw35QeJ9+SM5v5umzScfD4PfA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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d10a05fa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma     lambda  max_depth  min_child_weight\n",
      "0           0.109278  0.002671  80.039129        3.0         34.416378\n",
      "Score: 0.7883\n",
      "Time: 91.15 seconds\n",
      "Score: 0.8026\n",
      "Time: 126.81 seconds\n",
      "Score: 0.7747\n",
      "Time: 73.69 seconds\n",
      "Score: 0.7804\n",
      "Time: 89.77 seconds\n",
      "Score: 0.7707\n",
      "Time: 72.84 seconds\n",
      "Score: 0.7883\n",
      "Score: 0.8026\n",
      "Score: 0.7747\n",
      "Score: 0.7804\n",
      "Score: 0.7707\n",
      "Score: 0.7883\n",
      "Score: 0.8026\n",
      "Score: 0.7747\n",
      "Score: 0.7804\n",
      "Score: 0.7707\n"
     ]
    },
    {
     "data": {
      "image/png": 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6k/LDo7R/OWn/+CV1d3CSkkD9zQgLyXc1NtjWd3Nc/YK0bxfrDR/Jn8HjpH3qM4zcnr4/\nab95hJRUb6XurifgG8DJhem/o5CwWsRBw/11rPs5Ken8Z36N15NOxoYTylzSCd7jpJP9jxfWa7rv\n0+Z+UP83XAAflaTZpEQwCfhCRCyuW/4cUj3hCFIi+FhELBltXUmLSAfV4RrJ30TEdS07M/Kci0i3\ns4738s12EZIOBb4SEa/udl8mmqQ9SCc3J0bhy41lJ2m4z5u63ZfxkvQy4LMRcUJh3nXA2ZFqNz2n\nZULJ47NrSGOiG0hXH3MjYnWhzXmke6TPy0XUuxi5zG24bk4Ij8dIEWpsHXdCMTMrlXaK8seTvj27\nNiK2kIYp5tS1CUbuNtmXdKfFU22su8MFbTMzK4d2EsqhFG7PJRVK6++8+RRwrKQNpHHGs9tcd4Gk\nlZI+L2nKWDoeEef76sTMrDw6ddvwScCKiDiE9E3MTxd/YK2Ji0lFun5SsbV0P4xoZmbtm9xGmwco\n3NdMupvlgbo27wD+CdKXziTdS7oLpOm6EfHzwvz/R7qrZzuSWt81YGZm24mICS0rtJNQlgNHSppG\nuu1sLunW4KK1pO8RfE/p/2U4inRr56PN1pV0cEQ8lNc/jXQ7Y0Pt3Ilm7RkaGmJoaKjb3TDbjmOz\ns9J3vSdWy4QSEVslLQBuYOTW31WS5qfFcQnpXu4lkm7Lq30gIh4BaLRubnOhpH7SnWD3kb6QZTvZ\nfffd1+0umDXk2Ky+dq5QyN8PObpu3ucKjx8k1VHaWjfPd0HdzGwX4v8CuMcMDg52uwtmDTk2q6+t\nb8p3k6Qoex/NzMpG0oQX5X2F0mNqtVq3u2DWkGOz+pxQzMysIzzkZWa2C/KQl5mZVZYTSo/xOLWV\nlWOz+pxQzMysI1xDMTPbBbmGYmZmleWE0mM8Tm1l5disvrZ+y8uqZby/MuqhRZsI44lPx2Y1+Apl\nFxQRTf8WLWq+zGwiODZ3XS7K9xgJ/HZaGTk2O8tFeZsAtW53wKyJWrc7YDvICcXMzDrCQ149xsMK\nVlaOzc7ykJeZmVWWE0qPOeOMWre7YNaQY7P6nFB6jP+XVSsrx2b1uYZiZrYLcg3FzMwqywmlx/j3\nkqysHJvV11ZCkTRb0mpJaySd22D5cyRdI2mlpNslDbZaV9JUSTdIukvS9ZKmdOQVmZlZV7RMKJIm\nAZ8CTgJeAsyT9OK6Zu8B7oyIfuAPgI9Jmtxi3YXAf0TE0cB3gPM68YJsdLXaQLe7YNaQY7P62rlC\nOR64OyLWRsQWYBkwp65NAPvmx/sCD0fEUy3WnQMszY+XAqeO/2VYu84/v9s9MGvMsVl97SSUQ4F1\nhen1eV7Rp4BjJW0AbgXObmPdgyJiI0BEPAQcOLau2/jUut0BsyZq3e6A7aBO/X8oJwErIuK1kl4I\nfEvScWPcRtN7gwcHB5k+fToAfX199Pf3MzAwAIwU8jzd3jSspFYrT3887WlPd2a6VquxZMkSgG3H\ny4nW8nsokmYAQxExO08vBCIiFhfafAP4p4j4Xp7+NnAuKWE1XFfSKmAgIjZKOhi4MSKOafD8/h5K\nB/n3kqysHJudVdbvoSwHjpQ0TdIewFzgmro2a4FZAJIOAo4C7mmx7jXAYH58BnD1DrwOMzPrspYJ\nJSK2AguAG4A7gWURsUrSfEln5mYfAX5P0m3At4APRMQjzdbN6ywGXifpLuBE4IJOvjBrzL+XZGXl\n2Kw+//RKj6nVaoV6ill5ODY7qxtDXk4oZma7oLLWUMzMzFpyQukxw7cZmpWNY7P6nFDMzKwjnFB6\njH8vycrKsVl9Lsr3GH95zMrKsdlZLsrbBKh1uwNmTdS63QHbQU4oZmbWER7y6jEeVrCycmx2loe8\nzMysspxQeox/L8nKyrFZfU4oPWZwsNs9MGvMsVl9rqGYme2CXEMxM7PKckLpMf69JCsrx2b1OaGY\nmVlHOKH0GP9ekpWVY7P6XJTvMf7ymJWVY7OzXJS3CVDrdgfMmqh1uwO2g5xQzMysIzzk1WM8rGBl\n5djsLA95mZlZZTmh9Bj/XpKVlWOz+tpKKJJmS1otaY2kcxssf7+kFZJukXS7pKck9eVlZ+d5t0s6\nu7DOIknr8zq3SJrduZdlzfj3kqysHJvV17KGImkSsAY4EdgALAfmRsTqJu1PBs6JiFmSXgJcDrwK\neAq4DpgfEfdIWgQ8HhEXtXh+11DMzMaorDWU44G7I2JtRGwBlgFzRmk/j5REAI4BfhgRv4mIrcBN\nwGmFthP6Ys3MbOdpJ6EcCqwrTK/P87YjaS9gNnBlnnUH8BpJUyXtDbwBOLywygJJKyV9XtKUMffe\nxsy/l2Rl5disvskd3t4pwM0RsRkgIlZLWgx8C3gCWAFszW0vBj4UESHpI8BFwDsbbXRwcJDp06cD\n0NfXR39/PwMDA8BIEHq6vemVK1eWqj+e9rSnOzNdq9VYsmQJwLbj5URrp4YyAxiKiNl5eiEQEbG4\nQdurgCsiYlmTbf0DsC4iPls3fxpwbUQc12Ad11A6aGgo/ZmVjWOzs7pRQ2knoewG3EUqyj8I/AiY\nFxGr6tpNAe4BDouIJwvzD4iIn0s6glSUnxERj0k6OCIeym3eB7wqIk5v8PxOKB3kL49ZWTk2O6sb\nCaXlkFdEbJW0ALiBVHP5QkSskjQ/LY5LctNTgeuLySS7UtJ+wBbg3RHxWJ5/oaR+4GngPmD+jr8c\na60GDHS5D2aN1HBsVpt/eqXHSDUiBrrdDbPtODY7q5RDXt3mhNJZHlawsnJsdlZZv4diZmbWkhNK\nj/HvJVlZOTarzwmlx/j3kqysHJvV5xqKmdkuyDUUMzOrrEoklKHaUNP5Ol/b/bn9KO0HS9Yft3f7\nQmyWqj8Vb98NHvLqMbVabdvvAJmViWOzszzkZTtdrTbQ7S6YNeTYrD5fofQY+ctjVlKOzc7yFYpN\ngFq3O2DWRK3bHbAd5IRiZmYd4SGvHuNhBSsrx2ZnecjLzMwqywmlx/j3kqysHJvV54TSY/x7SVZW\njs3qcw3FzGwX5BqKmZlVlhNKj6nVat3ugllDjs3qc0IxM7OOcELpMf69JCsrx2b1uSjfY/zlMSsr\nx2ZnuShvE6DW7Q6YNVHrdgdsB7WVUCTNlrRa0hpJ5zZY/n5JKyTdIul2SU9J6svLzs7zbpd0VmGd\nqZJukHSXpOslTencyzIzs4nWcshL0iRgDXAisAFYDsyNiNVN2p8MnBMRsyS9BLgceBXwFHAdMD8i\n7pG0GHg4Ii7MSWpqRCxssD0PeXWQhxWsrBybnVXWIa/jgbsjYm1EbAGWAXNGaT+PlEQAjgF+GBG/\niYitwE3AaXnZHGBpfrwUOHWsnTczs/JoJ6EcCqwrTK/P87YjaS9gNnBlnnUH8Jo8vLU38Abg8Lzs\noIjYCBARDwEHjr37Nlb+vSQrK8dm9U3u8PZOAW6OiM0AEbE6D219C3gCWAFsbbJu04vdwcFBpk+f\nDkBfXx/9/f3b/u/p4S9Debq96f7+ldRq5emPpz09PD04WK7+VG26VquxZMkSgG3Hy4nWTg1lBjAU\nEbPz9EIgImJxg7ZXAVdExLIm2/oHYF1EfFbSKmAgIjZKOhi4MSKOabCOayhmZmNU1hrKcuBISdMk\n7QHMBa6pb5Tv0poJXF03/4D87xHAHwNfzouuAQbz4zPq1zMzs2ppmVByMX0BcANwJ7AsIlZJmi/p\nzELTU4HrI+LJuk1cKekOUsJ4d0Q8lucvBl4n6S7SHWQX7OBrsTYMXyKblY1js/raqqFExHXA0XXz\nPlc3vZSRu7aK83+/yTYfAWa13VMzMys1f1O+x/j3kqysHJvV59/y6jH+8piVlWOzs8palLddSq3b\nHTBrotbtDtgOckIxM7OO8JBXj/GwgpWVY7OzPORlZmaV5YRSYfvtl87qxvIHtTG132+/br9Kq6KJ\niE3HZ/k4oVTYpk1piGAsfzfeOLb2mzZ1+1VaFU1EbDo+y8c1lAqbiDFnj2vbeExU3Dg+m3MNxczM\nKssJpcf495KsrByb1eeEYmZmHeEaSoW5hmJl5RpK97mGYmZmleWE0mM8Tm1l5disPicUMzPrCNdQ\nKsw1FCsr11C6zzUUMzOrLCeUHuNxaisrx2b1OaGYmVlHuIZSYa6hWFm5htJ9rqGYmVllOaH0GI9T\nW1k5NquvrYQiabak1ZLWSDq3wfL3S1oh6RZJt0t6SlJfXvY+SXdIuk3SlyTtkecvkrQ+r3OLpNmd\nfWlmZjaRWtZQJE0C1gAnAhuA5cDciFjdpP3JwDkRMUvSIcDNwIsj4reSvgJ8MyIulbQIeDwiLmrx\n/K6hNOEaipWVayjdV9YayvHA3RGxNiK2AMuAOaO0nwdcXpjeDXi2pMnA3qSkNGxCX6yZme087SSU\nQ4F1hen1ed52JO0FzAauBIiIDcDHgPuBB4DNEfEfhVUWSFop6fOSpoyj/zZGHqe2snJsVt/kDm/v\nFODmiNgMkOsoc4BpwKPA1ySdHhFfBi4GPhQRIekjwEXAOxttdHBwkOnTpwPQ19dHf38/AwMDwEgQ\nerq96ZUrV46pPdSo1crTf09XYxom5vkcnyPTtVqNJUuWAGw7Xk60dmooM4ChiJidpxcCERGLG7S9\nCrgiIpbl6TcBJ0XEu/L024DfiYgFdetNA66NiOMabNM1lCZcQ7Gycg2l+8paQ1kOHClpWr5Day5w\nTX2jPGQ1E7i6MPt+YIakPSWJVNhfldsfXGh3GnDH+F6CmZmVQcuEEhFbgQXADcCdwLKIWCVpvqQz\nC01PBa6PiCcL6/4I+BqwAriVVIS/JC++MN9KvJKUiN7XiRdkoxsZkjArF8dm9bVVQ4mI64Cj6+Z9\nrm56KbC0wbrnA+c3mP/2MfXUzMxKzb/lVWGuoVhZuYbSfWWtoZiZmbXkhNJjPE5tZeXYrD4PeVVY\nu5f7F5x5Jr9eswaA+zZvZnpfHwB7HnUUCy+5ZLRVPaRg4zIRsTmW5+lF3Rjy6vQXG62Efr1mDUM3\n3bTd/KGJ74rZMzg2dy0e8jIzs46oxJCXT1c66F7g+d3uhFkDjs3OGmLCh7wqkVDK3sduaXf8eGhg\nYNuwQo3hX1mCoZkzGWpRCPUYtY3HRMTmWJ6nF/m2YdvpBrrdAbMmBrrdAdthLsr3gD2POqrhqOGe\nRx010V0xewbH5q7FQ14VNp7L/VqtVvjp753zHGYTEZvjfZ5e4SEvMzOrLF+hVJh/y8vKyr/l1X2+\nQjEzs8pyQukx/r0kKyvHZvU5oZiZWUe4hlJhrqFYWbmG0n2uoZiZWWU5ofQYj1NbWTk2q88JxczM\nOsI1lApzDcXKyjWU7nMNxczMKssJpcd4nNrKyrFZfW0lFEmzJa2WtEbSuQ2Wv1/SCkm3SLpd0lOS\n+vKy90m6Q9Jtkr4kaY88f6qkGyTdJel6SVM6+9LMzGwitayhSJoErAFOBDYAy4G5EbG6SfuTgXMi\nYpakQ4CbgRdHxG8lfQX4ZkRcKmkx8HBEXJiT1NSIWNhge66hNKMJGh71+29jNVGxCY7PJspaQzke\nuDsi1kbEFmAZMGeU9vOAywvTuwHPljQZ2Bt4IM+fAyzNj5cCp46l4wYi0s60E/+Ed1Ybu4mITcdn\n+bSTUA4F1hWm1+d525G0FzAbuBIgIjYAHwPuJyWSzRHx7dz8wIjYmNs9BBw4nhdgY+Nxaisrx2b1\ndfp/bDwFuDkiNgPkOsocYBrwKPA1SadHxJcbrNv0VGNwcJDp06cD0NfXR39//7b/iGc4CD3d3vTK\nlSvH1B5q1Grl6b+nqzE9/B/67uznc3yOTNdqNZYsWQKw7Xg50dqpocwAhiJidp5eCERELG7Q9irg\niohYlqffBJwUEe/K028DficiFkhaBQxExEZJBwM3RsQxDbbpGkoT/h6KlZW/h9J9Za2hLAeOlDQt\n36E1F7imvlG+S2smcHVh9v3ADEl7ShKpsL8qL7sGGMyPz6hbz8zMKqZlQomIrcAC4AbgTmBZRKyS\nNF/SmYWmpwLXR8SThXV/BHwNWAHcCgi4JC9eDLxO0l2kRHNBB16PtTAyJGFWLo7N6murhhIR1wFH\n1837XN30Ukbu2irOPx84v8H8R4BZY+msmZmVl3/Lq8JcQ7Gycg2l+8paQzEzM2vJCaXHeJzaysqx\nWX1OKGbDC3r3AAAGS0lEQVRm1hGuoVSYayhWVq6hdJ9rKGZmVllOKD3G49RWVo7N6nNCMTOzjnAN\npcJcQ7Gycg2l+1xDMTOzynJC6TEep7aycmxWnxOKmZl1hGsoFeYaipWVayjd5xqKmZlVlhNKj/E4\ntZWVY7P6nFDMzKwjXEOpMNdQrKxcQ+k+11DMzKyynFB6jMeprawcm9XnhGJmZh3hGkqFuYZiZeUa\nSve5hmJmZpXlhNJjPE5tZeXYrL62Eoqk2ZJWS1oj6dwGy98vaYWkWyTdLukpSX2SjirMXyHpUUln\n5XUWSVqfl90iaXanX5yZmU2cljUUSZOANcCJwAZgOTA3IlY3aX8ycE5EzGqwnfXA8RGxXtIi4PGI\nuKjF87uG0oRrKFZWrqF0X1lrKMcDd0fE2ojYAiwD5ozSfh5weYP5s4CfRcT6wrwJfbFmZrbztJNQ\nDgXWFabX53nbkbQXMBu4ssHit7B9olkgaaWkz0ua0kZfbAd5nNrKyrFZfZM7vL1TgJsjYnNxpqTd\ngTcCCwuzLwY+FBEh6SPARcA7G210cHCQ6dOnA9DX10d/fz8DAwPASBB6ur3plStXjqk91KjVytN/\nT1djGibm+RyfI9O1Wo0lS5YAbDteTrR2aigzgKGImJ2nFwIREYsbtL0KuCIiltXNfyPw7uFtNFhv\nGnBtRBzXYJlrKE24hmJl5RpK93WjhtLOFcpy4Mh80H8QmEuqkzxDHrKaCby1wTa2q6tIOjgiHsqT\npwF3jKHflmknh8vUqTt3+7br2tmxCY7PsmmZUCJiq6QFwA2kmssXImKVpPlpcVySm54KXB8RTxbX\nl7Q3qSB/Zt2mL5TUDzwN3AfM36FX0oPGc2Ym1YgY6HhfzIocm73JP73SY7zTWlk5NjurG0NeTig9\nxmPOVlaOzc4q6/dQzMzMWnJC6Tm1bnfArIlatztgO8gJpceccUa3e2DWmGOz+lxDMTPbBZX1eyhd\nN1QbYmhgqOH88286f7v5i2Yucnu3d3u3d/sJ5iuUHlOr1Qo/W2FWHo7NzvJdXmZmVlm+QjEz2wX5\nCsV2uqGhbvfArDHHZvX5CqXH+OctrKwcm53lKxQzM6ssX6H0GP9ekpWVY7OzfIViZmaV5YTSc2rd\n7oBZE7Vud8B2kBNKj/HvJVlZOTarzzUUM7NdkGsoZmZWWU4oPaZWq3W7C2YNOTarzwnFzMw6wjUU\nM7NdkGsottP595KsrByb1dfWFYqk2cAnSAnoCxGxuG75+4G3AgHsDhwD7A8cCHwlzxfwAuCDEfFJ\nSVPzsmnAfcCbI+LRBs/tK5QO8u8lWVk5NjurG1coLROKpEnAGuBEYAOwHJgbEaubtD8ZOCciZjXY\nznrg+IhYL2kx8HBEXCjpXGBqRCxssD0nlDGSxhdDfp9tIownPh2bY1fWIa/jgbsjYm1EbAGWAXNG\naT8PuLzB/FnAzyJifZ6eAyzNj5cCp7bXZWslIpr+LVq0qOkys4ng2Nx1tZNQDgXWFabX53nbkbQX\nMBu4ssHit/DMRHNgRGwEiIiHSMNjZmZWUZ0uyp8C3BwRm4szJe0OvBH46ijr+jRkAtx3333d7oJZ\nQ47N6pvcRpsHgCMK04fleY3MpfFw1+uBn0TEzwvzNko6KCI2SjoY+J9mHRhvTcAaW7p0aetGZl3g\n2Ky2dhLKcuBISdOAB0lJY159I0lTgJmku73qNaqrXAMMAouBM4CrGz35RBeVzMxsfMZy2/A/M3Lb\n8AWS5gMREZfkNmcAJ0XE6XXr7g2sBV4QEY8X5u8HXAEcnpe/uX6ozMzMqqP035Q3M7Nq8Dfle4Sk\nL0jaKOm2bvfFrEjSYZK+I+lOSbdLOqvbfbLx8RVKj5D0auAJ4NKIOK7b/TEblm/KOTgiVkraB/gJ\nMKfZl6etvHyF0iMi4mZgU7f7YVYvIh6KiJX58RPAKpp8183KzQnFzEpD0nSgH/hhd3ti4+GEYmal\nkIe7vgacna9UrGKcUMys6yRNJiWTyyKi4XfSrPycUHqL8p9Z2XwR+O+I+Odud8TGzwmlR0j6MvB9\n4ChJ90t6R7f7ZAYg6QTSL2y8VtIKSbfkL1Nbxfi2YTMz6whfoZiZWUc4oZiZWUc4oZiZWUc4oZiZ\nWUc4oZiZWUc4oZiZWUc4oZiZWUc4oZiZWUf8fxaI7SoKWqCvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d119c40b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma      lambda  max_depth  min_child_weight\n",
      "0           0.097794  0.084481  216.673046        2.0          5.728526\n",
      "Score: 0.7878\n",
      "Time: 170.02 seconds\n",
      "Score: 0.8026\n",
      "Time: 198.97 seconds\n",
      "Score: 0.7748\n",
      "Time: 143.73 seconds\n",
      "Score: 0.7808\n",
      "Time: 121.87 seconds\n",
      "Score: 0.7694\n",
      "Time: 132.42 seconds\n",
      "Score: 0.7878\n",
      "Score: 0.8026\n",
      "Score: 0.7748\n",
      "Score: 0.7808\n",
      "Score: 0.7694\n",
      "Score: 0.7878\n",
      "Score: 0.8026\n",
      "Score: 0.7748\n",
      "Score: 0.7808\n",
      "Score: 0.7694\n"
     ]
    },
    {
     "data": {
      "image/png": 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DfXNs8ZBXFxvJ5X6lUik8+nvnbMNsNPrmSLfTKzzkZWZmXctXKF3Mz/KysvKz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d119c40b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma     lambda  max_depth  min_child_weight\n",
      "0           0.102159  0.003582  12.542904        3.0          3.005051\n",
      "Score: 0.7867\n",
      "Time: 73.27 seconds\n",
      "Score: 0.8013\n",
      "Time: 93.10 seconds\n",
      "Score: 0.7743\n",
      "Time: 61.55 seconds\n",
      "Score: 0.7806\n",
      "Time: 75.10 seconds\n",
      "Score: 0.7719\n",
      "Time: 52.85 seconds\n",
      "Score: 0.7867\n",
      "Score: 0.8013\n",
      "Score: 0.7743\n",
      "Score: 0.7806\n",
      "Score: 0.7719\n",
      "Score: 0.7867\n",
      "Score: 0.8013\n",
      "Score: 0.7743\n",
      "Score: 0.7806\n",
      "Score: 0.7719\n"
     ]
    },
    {
     "data": {
      "image/png": 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RQ/HpipnZCM1jzHMolQgoZW9jp7SakJw3bdrmYYWtpk+dyrxhbtV00tNGYyz6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\n8f8B78Nv/g2NHJoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d10007afd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma     lambda  max_depth  min_child_weight\n",
      "0            0.14881  0.150535  97.386409        3.0          8.220372\n",
      "Score: 0.7885\n",
      "Time: 97.55 seconds\n",
      "Score: 0.8027\n",
      "Time: 138.12 seconds\n",
      "Score: 0.7759\n",
      "Time: 87.13 seconds\n",
      "Score: 0.7819\n",
      "Time: 91.39 seconds\n",
      "Score: 0.7691\n",
      "Time: 77.94 seconds\n",
      "Score: 0.7885\n",
      "Score: 0.8027\n",
      "Score: 0.7759\n",
      "Score: 0.7819\n",
      "Score: 0.7691\n",
      "Score: 0.7885\n",
      "Score: 0.8027\n",
      "Score: 0.7759\n",
      "Score: 0.7819\n",
      "Score: 0.7691\n"
     ]
    },
    {
     "data": {
      "image/png": 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J39VTpC6YTxbbBOlOyg8Ns11HkfaPX1J1BycpCKyoKj+PfFdjjWV9J7erX5D27eJ4w4fz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\nWsIBxczMWsIBxczMWsIBxczMWuL/A5eNykbj+ve2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d101f796d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma    lambda  max_depth  min_child_weight\n",
      "0           0.136821  0.155783  25.48591        2.0          2.925309\n",
      "Score: 0.7859\n",
      "Time: 95.37 seconds\n",
      "Score: 0.8027\n",
      "Time: 120.59 seconds\n",
      "Score: 0.7730\n",
      "Time: 90.64 seconds\n",
      "Score: 0.7804\n",
      "Time: 92.44 seconds\n",
      "Score: 0.7704\n",
      "Time: 78.11 seconds\n",
      "Score: 0.7859\n",
      "Score: 0.8027\n",
      "Score: 0.7730\n",
      "Score: 0.7804\n",
      "Score: 0.7704\n",
      "Score: 0.7859\n",
      "Score: 0.8027\n",
      "Score: 0.7730\n",
      "Score: 0.7804\n",
      "Score: 0.7704\n"
     ]
    },
    {
     "data": {
      "image/png": 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P15Ee56Sg81+57TeRbk0fCihTSSd4j5NO9j9RWK7psd/Ovjf6G0qADytfEn6SFOU+FxH1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\nqhNlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d119d7e9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma    lambda  max_depth  min_child_weight\n",
      "0           0.115023  0.013079  4.440365        3.0         13.077846\n",
      "Score: 0.7869\n",
      "Time: 70.60 seconds\n",
      "Score: 0.8010\n",
      "Time: 101.97 seconds\n",
      "Score: 0.7734\n",
      "Time: 53.50 seconds\n",
      "Score: 0.7785\n",
      "Time: 75.15 seconds\n",
      "Score: 0.7708\n",
      "Time: 52.55 seconds\n",
      "Score: 0.7869\n",
      "Score: 0.8010\n",
      "Score: 0.7734\n",
      "Score: 0.7785\n",
      "Score: 0.7708\n",
      "Score: 0.7869\n",
      "Score: 0.8010\n",
      "Score: 0.7734\n",
      "Score: 0.7785\n",
      "Score: 0.7708\n"
     ]
    },
    {
     "data": {
      "image/png": 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3zfHFAaUH/HrVKgZvvRWAGkP/UwUMdqc5Zs9w3xxfPOTVY/q73QCzJvq73QDb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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d119d5beb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma      lambda  max_depth  min_child_weight\n",
      "0           0.107469  0.004267  392.485514        3.0          6.765868\n",
      "Score: 0.7873\n",
      "Time: 161.51 seconds\n",
      "Score: 0.8042\n",
      "Time: 161.31 seconds\n",
      "Score: 0.7755\n",
      "Time: 112.35 seconds\n",
      "Score: 0.7828\n",
      "Time: 108.22 seconds\n",
      "Score: 0.7691\n",
      "Time: 101.72 seconds\n",
      "Score: 0.7873\n",
      "Score: 0.8042\n",
      "Score: 0.7755\n",
      "Score: 0.7828\n",
      "Score: 0.7691\n",
      "Score: 0.7873\n",
      "Score: 0.8042\n",
      "Score: 0.7755\n",
      "Score: 0.7828\n",
      "Score: 0.7691\n"
     ]
    },
    {
     "data": {
      "image/png": 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vYTYeY3Os79MvfNuwjUqgZ/x5zVH4r1mnxmNspvfZ+V/rPZ+hVJif5WVl5Wd5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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d10a044ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma      lambda  max_depth  min_child_weight\n",
      "0           0.142599  0.687548  138.877782        2.0         44.183643\n",
      "Score: 0.7853\n",
      "Time: 142.40 seconds\n",
      "Score: 0.8029\n",
      "Time: 179.19 seconds\n",
      "Score: 0.7732\n",
      "Time: 112.71 seconds\n",
      "Score: 0.7798\n",
      "Time: 144.33 seconds\n",
      "Score: 0.7678\n",
      "Time: 115.84 seconds\n",
      "Score: 0.7853\n",
      "Score: 0.8029\n",
      "Score: 0.7732\n",
      "Score: 0.7798\n",
      "Score: 0.7678\n",
      "Score: 0.7853\n",
      "Score: 0.8029\n",
      "Score: 0.7732\n",
      "Score: 0.7798\n",
      "Score: 0.7678\n"
     ]
    },
    {
     "data": {
      "image/png": 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c28MjwIrCvJ8GPjjCeh1N2j9+RcMdnKSxoxUN5c8m39XYZFnfy/X4JWnfLo43fIs0BlGv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\nmVlXOKCYmVlX/H84ai3EoXZh2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d10a057080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma      lambda  max_depth  min_child_weight\n",
      "0           0.081449  0.107031  289.316956        3.0         27.426817\n",
      "Score: 0.7891\n",
      "Time: 113.88 seconds\n",
      "Score: 0.8023\n",
      "Time: 124.89 seconds\n",
      "Score: 0.7760\n",
      "Time: 102.14 seconds\n",
      "Score: 0.7812\n",
      "Time: 114.00 seconds\n",
      "Score: 0.7691\n",
      "Time: 63.97 seconds\n",
      "Score: 0.7891\n",
      "Score: 0.8023\n",
      "Score: 0.7760\n",
      "Score: 0.7812\n",
      "Score: 0.7691\n",
      "Score: 0.7891\n",
      "Score: 0.8023\n",
      "Score: 0.7760\n",
      "Score: 0.7812\n",
      "Score: 0.7691\n"
     ]
    },
    {
     "data": {
      "image/png": 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S1AXz8WKbID1JefEQ23U06fj4FTVPcJKCwLKa8nPJTzXWWdZ3c7v5JenYLo43fCjvgydI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\nM7O2cEAxM7O2+P8hPJfdk6B/3AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d10011c978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma     lambda  max_depth  min_child_weight\n",
      "0            0.10075  0.020036  60.099716        3.0          3.745635\n",
      "Score: 0.7870\n",
      "Time: 91.18 seconds\n",
      "Score: 0.8031\n",
      "Time: 115.82 seconds\n",
      "Score: 0.7748\n",
      "Time: 55.02 seconds\n",
      "Score: 0.7804\n",
      "Time: 96.86 seconds\n",
      "Score: 0.7716\n",
      "Time: 71.19 seconds\n",
      "Score: 0.7870\n",
      "Score: 0.8031\n",
      "Score: 0.7748\n",
      "Score: 0.7804\n",
      "Score: 0.7716\n",
      "Score: 0.7870\n",
      "Score: 0.8031\n",
      "Score: 0.7748\n",
      "Score: 0.7804\n",
      "Score: 0.7716\n"
     ]
    },
    {
     "data": {
      "image/png": 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O6Guy/Bk0OECSxsO+V/ievk8e3yyUOYZ0pfJ4/k5fXZh2GfChIbbrlaT945fU3cFJCgJ3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\n2sIBxczM2uJ/ATkTFl7pbHnlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d10011f860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma     lambda  max_depth  min_child_weight\n",
      "0           0.090886  0.034705  32.874282        4.0          0.254445\n",
      "Score: 0.7863\n",
      "Time: 54.24 seconds\n",
      "Score: 0.7999\n",
      "Time: 64.47 seconds\n",
      "Score: 0.7743\n",
      "Time: 55.22 seconds\n",
      "Score: 0.7800\n",
      "Time: 58.81 seconds\n",
      "Score: 0.7688\n",
      "Time: 41.13 seconds\n",
      "Score: 0.7863\n",
      "Score: 0.7999\n",
      "Score: 0.7743\n",
      "Score: 0.7800\n",
      "Score: 0.7688\n",
      "Score: 0.7863\n",
      "Score: 0.7999\n",
      "Score: 0.7743\n",
      "Score: 0.7800\n",
      "Score: 0.7688\n"
     ]
    },
    {
     "data": {
      "image/png": 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UKyvHULqvrDGUZcARkibmO7RmALc1Fsp3aU0Fbi1kPw5MkbSXJJEC+6vystuA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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d101db9c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma      lambda  max_depth  min_child_weight\n",
      "0           0.049405  0.017442  180.804296        2.0          0.725351\n",
      "Score: 0.7874\n",
      "Time: 147.08 seconds\n",
      "Score: 0.8024\n",
      "Time: 146.75 seconds\n",
      "Score: 0.7746\n",
      "Time: 127.58 seconds\n",
      "Score: 0.7798\n",
      "Time: 101.57 seconds\n",
      "Score: 0.7680\n",
      "Time: 90.62 seconds\n",
      "Score: 0.7874\n",
      "Score: 0.8024\n",
      "Score: 0.7746\n",
      "Score: 0.7798\n",
      "Score: 0.7680\n",
      "Score: 0.7874\n",
      "Score: 0.8024\n",
      "Score: 0.7746\n",
      "Score: 0.7798\n",
      "Score: 0.7680\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d119c8c320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma     lambda  max_depth  min_child_weight\n",
      "0           0.120984  0.005762  96.764749        4.0          1.652537\n",
      "Score: 0.7875\n",
      "Time: 88.93 seconds\n",
      "Score: 0.8005\n",
      "Time: 108.13 seconds\n",
      "Score: 0.7760\n",
      "Time: 66.92 seconds\n",
      "Score: 0.7815\n",
      "Time: 93.44 seconds\n",
      "Score: 0.7690\n",
      "Time: 62.73 seconds\n",
      "Score: 0.7875\n",
      "Score: 0.8005\n",
      "Score: 0.7760\n",
      "Score: 0.7815\n",
      "Score: 0.7690\n",
      "Score: 0.7875\n",
      "Score: 0.8005\n",
      "Score: 0.7760\n",
      "Score: 0.7815\n",
      "Score: 0.7690\n"
     ]
    },
    {
     "data": {
      "image/png": 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OxczMKstJ+R6w79FHPzVreN/mzUzt63uq3qybPDb3LJ7yqrCxnO7XarXCvyWx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\nxP8HDr6VlknkHTwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d1018b2470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma     lambda  max_depth  min_child_weight\n",
      "0           0.065965  0.009333  20.274372        3.0          14.24295\n",
      "Score: 0.7886\n",
      "Time: 117.02 seconds\n",
      "Score: 0.8007\n",
      "Time: 124.48 seconds\n",
      "Score: 0.7734\n",
      "Time: 60.29 seconds\n",
      "Score: 0.7808\n",
      "Time: 84.91 seconds\n",
      "Score: 0.7677\n",
      "Time: 64.96 seconds\n",
      "Score: 0.7886\n",
      "Score: 0.8007\n",
      "Score: 0.7734\n",
      "Score: 0.7808\n",
      "Score: 0.7677\n",
      "Score: 0.7886\n",
      "Score: 0.8007\n",
      "Score: 0.7734\n",
      "Score: 0.7808\n",
      "Score: 0.7677\n"
     ]
    },
    {
     "data": {
      "image/png": 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rwsZyul+r1Qr/Hvdz8xxm4zE2x/o8vaIbU14OKBXme3lZWfleXt3nHIqZmVWW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d101f79780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma    lambda  max_depth  min_child_weight\n",
      "0           0.110601  1.707333  10.47743        4.0          5.262636\n",
      "Score: 0.7832\n",
      "Time: 78.70 seconds\n",
      "Score: 0.8012\n",
      "Time: 71.93 seconds\n",
      "Score: 0.7741\n",
      "Time: 78.46 seconds\n",
      "Score: 0.7779\n",
      "Time: 86.63 seconds\n",
      "Score: 0.7689\n",
      "Time: 59.79 seconds\n",
      "Score: 0.7832\n",
      "Score: 0.8012\n",
      "Score: 0.7741\n",
      "Score: 0.7779\n",
      "Score: 0.7689\n",
      "Score: 0.7832\n",
      "Score: 0.8012\n",
      "Score: 0.7741\n",
      "Score: 0.7779\n",
      "Score: 0.7689\n"
     ]
    },
    {
     "data": {
      "image/png": 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4ASAiNgAfBx4EHgI2R8TXc/N9I2JjbvcIsO9IdsCGx7+XZGXlsVl9nf4eysnA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\n/hf9T/RdzhF8TwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d10a56e908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   colsample_bylevel     gamma   lambda  max_depth  min_child_weight\n",
      "0           0.129327  0.049662  6.09799        2.0          8.611968\n",
      "Score: 0.7869\n",
      "Time: 107.90 seconds\n",
      "Score: 0.8006\n",
      "Time: 127.94 seconds\n",
      "Score: 0.7725\n",
      "Time: 109.16 seconds\n",
      "Score: 0.7809\n",
      "Time: 134.55 seconds\n",
      "Score: 0.7690\n",
      "Time: 78.83 seconds\n",
      "Score: 0.7869\n",
      "Score: 0.8006\n",
      "Score: 0.7725\n",
      "Score: 0.7809\n",
      "Score: 0.7690\n",
      "Score: 0.7869\n",
      "Score: 0.8006\n",
      "Score: 0.7725\n",
      "Score: 0.7809\n",
      "Score: 0.7690\n"
     ]
    },
    {
     "data": {
      "image/png": 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lpM8DX4mIz0haDDwSEZfmIDU1IhY02J5zKE04h2Jl5RxK95U1h3IicE9ErIuI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\nmXXE/wL8RJSpqOh/RAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d10a56cef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min_child_weight</th>\n",
       "      <th>max_depth</th>\n",
       "      <th>lambda</th>\n",
       "      <th>gamma</th>\n",
       "      <th>colsample_bylevel</th>\n",
       "      <th>loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>31.290382</td>\n",
       "      <td>3.0</td>\n",
       "      <td>159.961967</td>\n",
       "      <td>0.016033</td>\n",
       "      <td>0.097941</td>\n",
       "      <td>-0.783951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2.901279</td>\n",
       "      <td>3.0</td>\n",
       "      <td>186.212660</td>\n",
       "      <td>0.094058</td>\n",
       "      <td>0.098315</td>\n",
       "      <td>-0.783803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>6.765868</td>\n",
       "      <td>3.0</td>\n",
       "      <td>392.485514</td>\n",
       "      <td>0.004267</td>\n",
       "      <td>0.107469</td>\n",
       "      <td>-0.783784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>11.338068</td>\n",
       "      <td>3.0</td>\n",
       "      <td>347.392483</td>\n",
       "      <td>0.012193</td>\n",
       "      <td>0.131110</td>\n",
       "      <td>-0.783757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>25.730014</td>\n",
       "      <td>4.0</td>\n",
       "      <td>152.850175</td>\n",
       "      <td>0.025001</td>\n",
       "      <td>0.072342</td>\n",
       "      <td>-0.783651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>8.220372</td>\n",
       "      <td>3.0</td>\n",
       "      <td>97.386409</td>\n",
       "      <td>0.150535</td>\n",
       "      <td>0.148810</td>\n",
       "      <td>-0.783614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>27.426817</td>\n",
       "      <td>3.0</td>\n",
       "      <td>289.316956</td>\n",
       "      <td>0.107031</td>\n",
       "      <td>0.081449</td>\n",
       "      <td>-0.783538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>3.745635</td>\n",
       "      <td>3.0</td>\n",
       "      <td>60.099716</td>\n",
       "      <td>0.020036</td>\n",
       "      <td>0.100750</td>\n",
       "      <td>-0.783379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34.416378</td>\n",
       "      <td>3.0</td>\n",
       "      <td>80.039129</td>\n",
       "      <td>0.002671</td>\n",
       "      <td>0.109278</td>\n",
       "      <td>-0.783345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.517258</td>\n",
       "      <td>3.0</td>\n",
       "      <td>45.546531</td>\n",
       "      <td>0.292144</td>\n",
       "      <td>0.090157</td>\n",
       "      <td>-0.783275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.463737</td>\n",
       "      <td>3.0</td>\n",
       "      <td>122.864554</td>\n",
       "      <td>0.051213</td>\n",
       "      <td>0.087899</td>\n",
       "      <td>-0.783103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.807670</td>\n",
       "      <td>3.0</td>\n",
       "      <td>53.059224</td>\n",
       "      <td>0.782896</td>\n",
       "      <td>0.122874</td>\n",
       "      <td>-0.783099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.537734</td>\n",
       "      <td>2.0</td>\n",
       "      <td>83.187169</td>\n",
       "      <td>0.284368</td>\n",
       "      <td>0.079391</td>\n",
       "      <td>-0.783060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5.728526</td>\n",
       "      <td>2.0</td>\n",
       "      <td>216.673046</td>\n",
       "      <td>0.084481</td>\n",
       "      <td>0.097794</td>\n",
       "      <td>-0.783057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.055752</td>\n",
       "      <td>3.0</td>\n",
       "      <td>46.961479</td>\n",
       "      <td>0.023728</td>\n",
       "      <td>0.146283</td>\n",
       "      <td>-0.783008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>3.005051</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.542904</td>\n",
       "      <td>0.003582</td>\n",
       "      <td>0.102159</td>\n",
       "      <td>-0.782950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.320332</td>\n",
       "      <td>4.0</td>\n",
       "      <td>119.438209</td>\n",
       "      <td>2.815963</td>\n",
       "      <td>0.062041</td>\n",
       "      <td>-0.782928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1.652537</td>\n",
       "      <td>4.0</td>\n",
       "      <td>96.764749</td>\n",
       "      <td>0.005762</td>\n",
       "      <td>0.120984</td>\n",
       "      <td>-0.782878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.586639</td>\n",
       "      <td>2.0</td>\n",
       "      <td>35.745627</td>\n",
       "      <td>0.207450</td>\n",
       "      <td>0.071358</td>\n",
       "      <td>-0.782790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.177725</td>\n",
       "      <td>3.0</td>\n",
       "      <td>218.150352</td>\n",
       "      <td>0.377860</td>\n",
       "      <td>0.081782</td>\n",
       "      <td>-0.782729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.262995</td>\n",
       "      <td>2.0</td>\n",
       "      <td>178.601985</td>\n",
       "      <td>0.478429</td>\n",
       "      <td>0.073960</td>\n",
       "      <td>-0.782674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.652312</td>\n",
       "      <td>4.0</td>\n",
       "      <td>27.053301</td>\n",
       "      <td>1.481315</td>\n",
       "      <td>0.136027</td>\n",
       "      <td>-0.782537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2.925309</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.485910</td>\n",
       "      <td>0.155783</td>\n",
       "      <td>0.136821</td>\n",
       "      <td>-0.782473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.214541</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.014364</td>\n",
       "      <td>0.031186</td>\n",
       "      <td>0.041898</td>\n",
       "      <td>-0.782454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.725351</td>\n",
       "      <td>2.0</td>\n",
       "      <td>180.804296</td>\n",
       "      <td>0.017442</td>\n",
       "      <td>0.049405</td>\n",
       "      <td>-0.782442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.887325</td>\n",
       "      <td>4.0</td>\n",
       "      <td>27.970595</td>\n",
       "      <td>0.008721</td>\n",
       "      <td>0.121014</td>\n",
       "      <td>-0.782441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37.291480</td>\n",
       "      <td>2.0</td>\n",
       "      <td>394.391123</td>\n",
       "      <td>0.002601</td>\n",
       "      <td>0.054110</td>\n",
       "      <td>-0.782297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>14.242950</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.274372</td>\n",
       "      <td>0.009333</td>\n",
       "      <td>0.065965</td>\n",
       "      <td>-0.782237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11.243902</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.241821</td>\n",
       "      <td>0.041511</td>\n",
       "      <td>0.042415</td>\n",
       "      <td>-0.782232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>13.077846</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.440365</td>\n",
       "      <td>0.013079</td>\n",
       "      <td>0.115023</td>\n",
       "      <td>-0.782134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>51.585159</td>\n",
       "      <td>2.0</td>\n",
       "      <td>272.806559</td>\n",
       "      <td>0.070813</td>\n",
       "      <td>0.096808</td>\n",
       "      <td>-0.782029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>8.611968</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.097990</td>\n",
       "      <td>0.049662</td>\n",
       "      <td>0.129327</td>\n",
       "      <td>-0.781975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.254445</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32.874282</td>\n",
       "      <td>0.034705</td>\n",
       "      <td>0.090886</td>\n",
       "      <td>-0.781875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>44.183643</td>\n",
       "      <td>2.0</td>\n",
       "      <td>138.877782</td>\n",
       "      <td>0.687548</td>\n",
       "      <td>0.142599</td>\n",
       "      <td>-0.781786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>45.755995</td>\n",
       "      <td>3.0</td>\n",
       "      <td>399.313684</td>\n",
       "      <td>0.061709</td>\n",
       "      <td>0.094337</td>\n",
       "      <td>-0.781401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.656034</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.170096</td>\n",
       "      <td>0.120680</td>\n",
       "      <td>0.086997</td>\n",
       "      <td>-0.781324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.983615</td>\n",
       "      <td>4.0</td>\n",
       "      <td>75.388268</td>\n",
       "      <td>0.103667</td>\n",
       "      <td>0.108000</td>\n",
       "      <td>-0.781191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>18.316502</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.654055</td>\n",
       "      <td>2.954674</td>\n",
       "      <td>0.055573</td>\n",
       "      <td>-0.781137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5.262636</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.477430</td>\n",
       "      <td>1.707333</td>\n",
       "      <td>0.110601</td>\n",
       "      <td>-0.781058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>19.999858</td>\n",
       "      <td>4.0</td>\n",
       "      <td>116.272894</td>\n",
       "      <td>6.559510</td>\n",
       "      <td>0.058400</td>\n",
       "      <td>-0.780992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4.159425</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.421553</td>\n",
       "      <td>0.007919</td>\n",
       "      <td>0.117038</td>\n",
       "      <td>-0.780977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34.273935</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.758499</td>\n",
       "      <td>0.107672</td>\n",
       "      <td>0.103231</td>\n",
       "      <td>-0.780586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.015256</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.133251</td>\n",
       "      <td>0.006160</td>\n",
       "      <td>0.053388</td>\n",
       "      <td>-0.780490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.631517</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.649955</td>\n",
       "      <td>0.005734</td>\n",
       "      <td>0.103638</td>\n",
       "      <td>-0.780027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16.976268</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.400008</td>\n",
       "      <td>1.196045</td>\n",
       "      <td>0.112730</td>\n",
       "      <td>-0.780008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>24.571331</td>\n",
       "      <td>3.0</td>\n",
       "      <td>64.979154</td>\n",
       "      <td>6.441836</td>\n",
       "      <td>0.066398</td>\n",
       "      <td>-0.779929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.628248</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.275718</td>\n",
       "      <td>0.003093</td>\n",
       "      <td>0.103523</td>\n",
       "      <td>-0.779797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.351166</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.733476</td>\n",
       "      <td>2.841783</td>\n",
       "      <td>0.059174</td>\n",
       "      <td>-0.779288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.176539</td>\n",
       "      <td>2.0</td>\n",
       "      <td>284.853980</td>\n",
       "      <td>2.676418</td>\n",
       "      <td>0.059903</td>\n",
       "      <td>-0.778568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.149984</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.223254</td>\n",
       "      <td>0.014513</td>\n",
       "      <td>0.126068</td>\n",
       "      <td>-0.778355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    min_child_weight  max_depth      lambda     gamma  colsample_bylevel  \\\n",
       "32         31.290382        3.0  159.961967  0.016033           0.097941   \n",
       "30          2.901279        3.0  186.212660  0.094058           0.098315   \n",
       "40          6.765868        3.0  392.485514  0.004267           0.107469   \n",
       "33         11.338068        3.0  347.392483  0.012193           0.131110   \n",
       "14         25.730014        4.0  152.850175  0.025001           0.072342   \n",
       "37          8.220372        3.0   97.386409  0.150535           0.148810   \n",
       "42         27.426817        3.0  289.316956  0.107031           0.081449   \n",
       "43          3.745635        3.0   60.099716  0.020036           0.100750   \n",
       "34         34.416378        3.0   80.039129  0.002671           0.109278   \n",
       "24          0.517258        3.0   45.546531  0.292144           0.090157   \n",
       "25          0.463737        3.0  122.864554  0.051213           0.087899   \n",
       "10          0.807670        3.0   53.059224  0.782896           0.122874   \n",
       "29          0.537734        2.0   83.187169  0.284368           0.079391   \n",
       "35          5.728526        2.0  216.673046  0.084481           0.097794   \n",
       "21          1.055752        3.0   46.961479  0.023728           0.146283   \n",
       "36          3.005051        3.0   12.542904  0.003582           0.102159   \n",
       "3           0.320332        4.0  119.438209  2.815963           0.062041   \n",
       "46          1.652537        4.0   96.764749  0.005762           0.120984   \n",
       "26          1.586639        2.0   35.745627  0.207450           0.071358   \n",
       "20          2.177725        3.0  218.150352  0.377860           0.081782   \n",
       "23          1.262995        2.0  178.601985  0.478429           0.073960   \n",
       "8           0.652312        4.0   27.053301  1.481315           0.136027   \n",
       "38          2.925309        2.0   25.485910  0.155783           0.136821   \n",
       "27          0.214541        3.0   17.014364  0.031186           0.041898   \n",
       "45          0.725351        2.0  180.804296  0.017442           0.049405   \n",
       "6           4.887325        4.0   27.970595  0.008721           0.121014   \n",
       "1          37.291480        2.0  394.391123  0.002601           0.054110   \n",
       "47         14.242950        3.0   20.274372  0.009333           0.065965   \n",
       "22         11.243902        3.0   15.241821  0.041511           0.042415   \n",
       "39         13.077846        3.0    4.440365  0.013079           0.115023   \n",
       "31         51.585159        2.0  272.806559  0.070813           0.096808   \n",
       "49          8.611968        2.0    6.097990  0.049662           0.129327   \n",
       "44          0.254445        4.0   32.874282  0.034705           0.090886   \n",
       "41         44.183643        2.0  138.877782  0.687548           0.142599   \n",
       "28         45.755995        3.0  399.313684  0.061709           0.094337   \n",
       "7           3.656034        3.0    3.170096  0.120680           0.086997   \n",
       "2           0.983615        4.0   75.388268  0.103667           0.108000   \n",
       "13         18.316502        2.0    1.654055  2.954674           0.055573   \n",
       "48          5.262636        4.0   10.477430  1.707333           0.110601   \n",
       "11         19.999858        4.0  116.272894  6.559510           0.058400   \n",
       "16          4.159425        4.0    7.421553  0.007919           0.117038   \n",
       "0          34.273935        4.0    1.758499  0.107672           0.103231   \n",
       "15          2.015256        4.0    3.133251  0.006160           0.053388   \n",
       "4           6.631517        4.0    9.649955  0.005734           0.103638   \n",
       "12         16.976268        4.0    2.400008  1.196045           0.112730   \n",
       "18         24.571331        3.0   64.979154  6.441836           0.066398   \n",
       "5           8.628248        4.0    6.275718  0.003093           0.103523   \n",
       "19          0.351166        4.0    2.733476  2.841783           0.059174   \n",
       "9           0.176539        2.0  284.853980  2.676418           0.059903   \n",
       "17          0.149984        4.0    1.223254  0.014513           0.126068   \n",
       "\n",
       "        loss  \n",
       "32 -0.783951  \n",
       "30 -0.783803  \n",
       "40 -0.783784  \n",
       "33 -0.783757  \n",
       "14 -0.783651  \n",
       "37 -0.783614  \n",
       "42 -0.783538  \n",
       "43 -0.783379  \n",
       "34 -0.783345  \n",
       "24 -0.783275  \n",
       "25 -0.783103  \n",
       "10 -0.783099  \n",
       "29 -0.783060  \n",
       "35 -0.783057  \n",
       "21 -0.783008  \n",
       "36 -0.782950  \n",
       "3  -0.782928  \n",
       "46 -0.782878  \n",
       "26 -0.782790  \n",
       "20 -0.782729  \n",
       "23 -0.782674  \n",
       "8  -0.782537  \n",
       "38 -0.782473  \n",
       "27 -0.782454  \n",
       "45 -0.782442  \n",
       "6  -0.782441  \n",
       "1  -0.782297  \n",
       "47 -0.782237  \n",
       "22 -0.782232  \n",
       "39 -0.782134  \n",
       "31 -0.782029  \n",
       "49 -0.781975  \n",
       "44 -0.781875  \n",
       "41 -0.781786  \n",
       "28 -0.781401  \n",
       "7  -0.781324  \n",
       "2  -0.781191  \n",
       "13 -0.781137  \n",
       "48 -0.781058  \n",
       "11 -0.780992  \n",
       "16 -0.780977  \n",
       "0  -0.780586  \n",
       "15 -0.780490  \n",
       "4  -0.780027  \n",
       "12 -0.780008  \n",
       "18 -0.779929  \n",
       "5  -0.779797  \n",
       "19 -0.779288  \n",
       "9  -0.778568  \n",
       "17 -0.778355  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parda = HpOpt(space, ParModelScore).sort_values([\"loss\"])\n",
    "parda.to_csv(\"{}/{}_par_xgb.csv\".format(path, title))\n",
    "paropt = parda.iloc[0, :-1].to_dict()\n",
    "parda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training\n",
    "Cross model and prediction results from optimized model variables and hyper-parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.7752\n",
      "Time: 7.33 seconds\n",
      "Score: 0.7814\n",
      "Time: 7.48 seconds\n",
      "Score: 0.7846\n",
      "Time: 7.03 seconds\n",
      "Score: 0.7950\n",
      "Time: 8.34 seconds\n",
      "Score: 0.7688\n",
      "Time: 6.70 seconds\n",
      "Score: 0.7751\n",
      "Time: 6.27 seconds\n",
      "Score: 0.7727\n",
      "Time: 7.01 seconds\n",
      "Score: 0.7658\n",
      "Time: 6.73 seconds\n",
      "Score: 0.7592\n",
      "Time: 6.90 seconds\n",
      "Score: 0.7727\n",
      "Time: 6.70 seconds\n",
      "Score: 0.7752\n",
      "Score: 0.7814\n",
      "Score: 0.7846\n",
      "Score: 0.7950\n",
      "Score: 0.7688\n",
      "Score: 0.7751\n",
      "Score: 0.7727\n",
      "Score: 0.7658\n",
      "Score: 0.7592\n",
      "Score: 0.7727\n",
      "Score: 0.7752\n",
      "Score: 0.7814\n",
      "Score: 0.7846\n",
      "Score: 0.7950\n",
      "Score: 0.7688\n",
      "Score: 0.7751\n",
      "Score: 0.7727\n",
      "Score: 0.7658\n",
      "Score: 0.7592\n",
      "Score: 0.7727\n"
     ]
    },
    {
     "data": {
      "image/png": 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ElkbEU3o2xOUsM7NJqUo5a3dgeWH69jxvA5K2ARYAF+dZTwb+IOmzudR1Vl7H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\nKc4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a6b726dda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "modellgrL = CrossTrain(x, y, irtL, Classifier, modeltype = lm.LogisticRegression,\n",
    "                      parmodel = {\"penalty\": \"l2\", \"C\": 0.003, \"class_weight\": 'balanced', \"solver\": \"sag\"})\n",
    "yt2plgrL = CrossValid(x, y, irtL, modellgrL)\n",
    "CrossScoreAnalysis(y, [yt2plgrL], irtL)\n",
    "yvplgrL = CrossPredict(xv, modellgrL)\n",
    "(pd.DataFrame({'score':ModelMPredict([yvplgrL])}, index = irv).reset_index().rename_axis({\"index\": \"Idx\"}, axis = 1)\n",
    " .set_index(\"Idx\").to_csv(\"{}/{}_op_lgr.csv\".format(path, title)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wcollgr = WeightCI(WeightModel, modellgrL)\n",
    "wcollgr.to_csv(\"{}/{}_w_lgr.csv\".format(path, title))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.7897\n",
      "Time: 467.33 seconds\n",
      "Score: 0.7970\n",
      "Time: 458.78 seconds\n",
      "Score: 0.7984\n",
      "Time: 359.29 seconds\n",
      "Score: 0.8153\n",
      "Time: 485.70 seconds\n",
      "Score: 0.7739\n",
      "Time: 278.61 seconds\n",
      "Score: 0.7850\n",
      "Time: 278.80 seconds\n",
      "Score: 0.7855\n",
      "Time: 385.56 seconds\n",
      "Score: 0.7799\n",
      "Time: 334.77 seconds\n",
      "Score: 0.7682\n",
      "Time: 271.12 seconds\n",
      "Score: 0.7763\n",
      "Time: 274.72 seconds\n",
      "Score: 0.7897\n",
      "Score: 0.7970\n",
      "Score: 0.7984\n",
      "Score: 0.8153\n",
      "Score: 0.7739\n",
      "Score: 0.7850\n",
      "Score: 0.7855\n",
      "Score: 0.7799\n",
      "Score: 0.7682\n",
      "Score: 0.7763\n",
      "Score: 0.7897\n",
      "Score: 0.7970\n",
      "Score: 0.7984\n",
      "Score: 0.8153\n",
      "Score: 0.7739\n",
      "Score: 0.7850\n",
      "Score: 0.7855\n",
      "Score: 0.7799\n",
      "Score: 0.7682\n",
      "Score: 0.7763\n"
     ]
    },
    {
     "data": {
      "image/png": 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nG2eStDcwB/hUYfRdwAmSdpUkUuf8iu1vtrVTvGoxKxvHZ7V0LGdFxCZJ5wFf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\nnETMzKxnTiJmZtaz/w9tjDuuJFSI+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a6d03b3d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "modelxgbL = CrossTrain(x, y, irtL, XGBoost, parmodel = {'colsample_bylevel': 0.07, 'eta': 0.02,\n",
    "                                                        'max_depth': 3, 'lambda': 50, 'min_child_weight': 1.5, 'gamma': 0.2})\n",
    "yt2pxgbL = CrossValid(x, y, irtL, modelxgbL)\n",
    "CrossScoreAnalysis(y, [yt2pxgbL], irtL)\n",
    "yvpxgbL = CrossPredict(xv, modelxgbL)\n",
    "(pd.DataFrame({'score':ModelMPredict([yvpxgbL])}, index = irv).reset_index().rename_axis({\"index\": \"Idx\"}, axis = 1)\n",
    "    .set_index(\"Idx\").to_csv(\"{}/{}_op_xgb.csv\".format(path, title)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a6b6c145f8>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Cd/cXgSWVio8Axkb3xwJHRvcPB8a7+2p3/wSYDezTMKGKiEhN6tuGvqW7lwG4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\nkhL/H0/J/ECCNkuVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a6b6e04b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def RowS_summary(da, rows, f = [\"sum\", \"count\"]):\n",
    "    grp = da.loc[rows.index].groupby(rows, axis = 0)\n",
    "    daop = pd.concat(map(lambda x: getattr(grp, x)(), f), keys = f, axis = 1)\n",
    "    return(daop)\n",
    "wcolxgb = WeightCI(WeightModel, modelxgbL)\n",
    "wcolxgb.to_csv(\"{}/{}_w_xgb.csv\".format(path, title))\n",
    "RowS_summary(wcolxgb.iloc[:,0], Col_group(wcolxgb.index), [\"sum\", \"count\"]).rename(\n",
    "    columns = {\"sum\": \"Weights\", \"count\": \"Variable Numbers\"}).plot(kind = \"bar\", title = \"XGB Weight Distribution of Variables\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pd.concat([wcollgr, wcolxgb], axis = 1, keys = [\"LR\", \"XGB\"]).to_csv(\"{}/{}_wcol.csv\".format(path, title))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "icx = list(wcolxgb.index[wcolxgb.iloc[:,0] > 0.0001])\n",
    "x = dac.loc[:, icx]\n",
    "x = x.apply(lambda x: x.fillna(x.median()),axis=0)\n",
    "x = (x.rank(pct = True)-0.5/x.shape[0]).apply(st.norm.ppf)\n",
    "# x = (x - x.mean())/x.std()\n",
    "xv = x.loc[irv].values\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 71999 samples, validate on 8000 samples\n",
      "Epoch 1/100\n",
      "71999/71999 [==============================] - 26s - loss: 0.3021 - val_loss: 0.2414\n",
      "Epoch 2/100\n",
      "71999/71999 [==============================] - 27s - loss: 0.2421 - val_loss: 0.2357\n",
      "Epoch 3/100\n",
      "71999/71999 [==============================] - 27s - loss: 0.2363 - val_loss: 0.2361\n",
      "Epoch 4/100\n",
      "71999/71999 [==============================] - 27s - loss: 0.2316 - val_loss: 0.2403\n",
      "Epoch 5/100\n",
      "71999/71999 [==============================] - 29s - loss: 0.2294 - val_loss: 0.2372\n",
      "Epoch 6/100\n",
      "71999/71999 [==============================] - 28s - loss: 0.2281 - val_loss: 0.2350\n",
      "Epoch 7/100\n",
      "71999/71999 [==============================] - 31s - loss: 0.2258 - val_loss: 0.2356\n",
      "Epoch 8/100\n",
      "71999/71999 [==============================] - 31s - loss: 0.2244 - val_loss: 0.2370\n",
      "Epoch 9/100\n",
      "71999/71999 [==============================] - 32s - loss: 0.2229 - val_loss: 0.2366\n",
      "Epoch 10/100\n",
      "71999/71999 [==============================] - 32s - loss: 0.2223 - val_loss: 0.2379\n",
      "Epoch 11/100\n",
      "71999/71999 [==============================] - 31s - loss: 0.2194 - val_loss: 0.2374\n",
      "Epoch 12/100\n",
      "71999/71999 [==============================] - 33s - loss: 0.2195 - val_loss: 0.2409\n",
      "Score: 0.7669\n",
      "Time: 365.53 seconds\n",
      "Train on 71999 samples, validate on 8000 samples\n",
      "Epoch 1/100\n",
      "71999/71999 [==============================] - 37s - loss: 0.3056 - val_loss: 0.2469\n",
      "Epoch 2/100\n",
      "71999/71999 [==============================] - 34s - loss: 0.2409 - val_loss: 0.2393\n",
      "Epoch 3/100\n",
      "71999/71999 [==============================] - 33s - loss: 0.2358 - val_loss: 0.2356\n",
      "Epoch 4/100\n",
      "63296/71999 [=========================>....] - ETA: 4s - loss: 0.2318"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-132-38dd28c5bc91>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m modeldnnL = CrossTrain(x, y, irtL, DNN, parmodel = {\"nhidlayer\": 1, \"rdrop\": 0.5, \"nhidnode\": 600, \"outnode\": 300, 'optimizer': \"rmsprop\", \n\u001b[1;32m----> 2\u001b[1;33m                                                     \"maxnorm\": 4, \"batch_size\": 64, \"earlystop\": 5})\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0myt2pdnnL\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCrossValid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mirtL\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodeldnnL\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mCrossScoreAnalysis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0myt2pdnnL\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mirtL\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0myvpdnnL\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCrossPredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodeldnnL\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-19-e5c0f2cdcfd8>\u001b[0m in \u001b[0;36mCrossTrain\u001b[1;34m(x, y, irtL, fmodel, **kwargs)\u001b[0m\n\u001b[0;32m     17\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mirtL\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m         \u001b[0mxt1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxt2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myt1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myt2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrainSet\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mirtL\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m         \u001b[0mmodelL\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfmodel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxt1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxt2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myt1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myt2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mseed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m     \u001b[1;32mreturn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodelL\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mCrossValid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mirtL\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodelL\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-102-69555e7dc01f>\u001b[0m in \u001b[0;36mDNN\u001b[1;34m(xt1, xt2, yt1, yt2, seed, parmodel)\u001b[0m\n\u001b[0;32m     23\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'binary_crossentropy'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpar\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'optimizer'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m     model.fit(xt1.astype(\"float32\"), yt1.astype(\"float32\"), nb_epoch=100, batch_size=par[\"batch_size\"], validation_data = (xt2, yt2), \n\u001b[1;32m---> 25\u001b[1;33m               callbacks = [EarlyStopping(monitor='val_loss', patience=par[\"earlystop\"])])\n\u001b[0m\u001b[0;32m     26\u001b[0m     \u001b[0mscore\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mScore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0myt2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mModelPredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxt2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     27\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Time: {:.2f} seconds\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mtimestart\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\recre\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight)\u001b[0m\n\u001b[0;32m   1009\u001b[0m                               \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1010\u001b[0m                               \u001b[0mval_f\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mval_f\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_ins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mval_ins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1011\u001b[1;33m                               callback_metrics=callback_metrics)\n\u001b[0m\u001b[0;32m   1012\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1013\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\recre\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[1;34m(self, f, ins, out_labels, batch_size, nb_epoch, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics)\u001b[0m\n\u001b[0;32m    747\u001b[0m                 \u001b[0mbatch_logs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'size'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    748\u001b[0m                 \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 749\u001b[1;33m                 \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    750\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    751\u001b[0m                     \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\recre\\Anaconda3\\lib\\site-packages\\keras\\backend\\theano_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m    486\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    487\u001b[0m         \u001b[1;32massert\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m{\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 488\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    489\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    490\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\recre\\Anaconda3\\lib\\site-packages\\theano-0.8.0-py3.5.egg\\theano\\compile\\function_module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    857\u001b[0m         \u001b[0mt0_fn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    858\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 859\u001b[1;33m             \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    860\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    861\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'position_of_error'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\recre\\Anaconda3\\lib\\site-packages\\theano-0.8.0-py3.5.egg\\theano\\gof\\op.py\u001b[0m in \u001b[0;36mrval\u001b[1;34m(p, i, o, n)\u001b[0m\n\u001b[0;32m    912\u001b[0m             \u001b[1;31m# default arguments are stored in the closure of `rval`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    913\u001b[0m             \u001b[1;32mdef\u001b[0m \u001b[0mrval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnode_input_storage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnode_output_storage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 914\u001b[1;33m                 \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    915\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mo\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    916\u001b[0m                     \u001b[0mcompute_map\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mo\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\recre\\Anaconda3\\lib\\site-packages\\theano-0.8.0-py3.5.egg\\theano\\tensor\\blas.py\u001b[0m in \u001b[0;36mperform\u001b[1;34m(self, node, inp, out)\u001b[0m\n\u001b[0;32m   1550\u001b[0m         \u001b[0mz\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1551\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1552\u001b[1;33m             \u001b[0mz\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1553\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1554\u001b[0m             \u001b[1;31m# The error raised by numpy has no shape information, we mean to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "modeldnnL = CrossTrain(x, y, irtL, DNN, parmodel = {\"nhidlayer\": 1, \"rdrop\": 0.5, \"nhidnode\": 600, \"outnode\": 300, 'optimizer': \"rmsprop\", \n",
    "                                                    \"maxnorm\": 4, \"batch_size\": 64, \"earlystop\": 5})\n",
    "yt2pdnnL = CrossValid(x, y, irtL, modeldnnL)\n",
    "CrossScoreAnalysis(y, [yt2pdnnL], irtL)\n",
    "yvpdnnL = CrossPredict(xv, modeldnnL)\n",
    "(pd.DataFrame({'score':ModelMPredict([yvpdnnL])}, index = irv).reset_index().rename_axis({\"index\": \"Idx\"}, axis = 1)\n",
    " .set_index(\"Idx\").to_csv(\"{}/{}_op_dnn.csv\".format(path, title)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Composite Models and Prediction\n",
    "Combine different types of models and get optimized weights for the composite prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.7897\n",
      "Score: 0.7752\n",
      "Score: 0.7970\n",
      "Score: 0.7814\n",
      "Score: 0.7984\n",
      "Score: 0.7846\n",
      "Score: 0.8153\n",
      "Score: 0.7950\n",
      "Score: 0.7739\n",
      "Score: 0.7688\n",
      "Score: 0.7850\n",
      "Score: 0.7751\n",
      "Score: 0.7855\n",
      "Score: 0.7727\n",
      "Score: 0.7799\n",
      "Score: 0.7658\n",
      "Score: 0.7682\n",
      "Score: 0.7592\n",
      "Score: 0.7763\n",
      "Score: 0.7727\n",
      "Score: 0.7913\n",
      "Score: 0.7985\n",
      "Score: 0.7989\n",
      "Score: 0.8147\n",
      "Score: 0.7770\n",
      "Score: 0.7862\n",
      "Score: 0.7861\n",
      "Score: 0.7803\n",
      "Score: 0.7702\n",
      "Score: 0.7785\n"
     ]
    },
    {
     "data": {
      "image/png": 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BEBFrgY8CdwK/Bu6JiP9s9EEjoyPoPG3zGhkd6ev0jHS2/OELhyv1fQdpekY6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\nETMza5uTiJmZtc1JxMzM2vb/AV06aTga9RZCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a66c5ead30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "score, score2, w = CrossScoreAnalysis(y, [yt2pxgbL, yt2plgrL], irtL, [0.9, 0.1], [\"XGB+LR\", \"XGB\", \"LR\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.7897\n",
      "Score: 0.7706\n",
      "Score: 0.7752\n",
      "Score: 0.7970\n",
      "Score: 0.7738\n",
      "Score: 0.7814\n",
      "Score: 0.7984\n",
      "Score: 0.7709\n",
      "Score: 0.7846\n",
      "Score: 0.8153\n",
      "Score: 0.7876\n",
      "Score: 0.7950\n",
      "Score: 0.7739\n",
      "Score: 0.7693\n",
      "Score: 0.7688\n",
      "Score: 0.7850\n",
      "Score: 0.7712\n",
      "Score: 0.7751\n",
      "Score: 0.7855\n",
      "Score: 0.7742\n",
      "Score: 0.7727\n",
      "Score: 0.7799\n",
      "Score: 0.7645\n",
      "Score: 0.7658\n",
      "Score: 0.7682\n",
      "Score: 0.7578\n",
      "Score: 0.7592\n",
      "Score: 0.7763\n",
      "Score: 0.7675\n",
      "Score: 0.7727\n",
      "Score: 0.7921\n",
      "Score: 0.7983\n",
      "Score: 0.7988\n",
      "Score: 0.8155\n",
      "Score: 0.7785\n",
      "Score: 0.7874\n",
      "Score: 0.7879\n",
      "Score: 0.7815\n",
      "Score: 0.7715\n",
      "Score: 0.7801\n"
     ]
    },
    {
     "data": {
      "image/png": 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4JiL+1OYzal+d1Wg0KvuTmVXFNN2qjNvlWa0qy9NAahAx1OswxlWH6qyOSUTS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\nUkgcSpRhfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a66c6009e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KkC6Fr6htN2+jdutj3j7uy8vX/vtmbfjP67nvsY0+hXTQv4N0sDiiNmw3UiH++xznd7vN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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a6621d5dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "score, score1, w = CrossScoreAnalysis(y, [yt2pxgbL, yt2pdnnL, yt2plgrL], irtL, [0.75, 0.20, 0.05], [\"XGB+Keras+LR\", \"XGB\", \"Keras\", \"LR\"])\n",
    "ScorePlot(np.vstack([score1, score2, score.T]).T, [\"XGB+Keras+LR\", \"XGB+LR\", \"XGB\", \"Keras\", \"LR\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def ModelWOpt(y, yt2pM, irtL, seed = 0):\n",
    "    iw = [\"w_{}\".format(2+i) for i in range(len(yt2pM)-1)]\n",
    "    space = dict(zip(*[iw, [hp.uniform(iw[i], 0, 1/len(yt2pM)) for i in range(len(yt2pM)-1)]]))\n",
    "    def Obj(w):\n",
    "        wtmp = [1-np.sum(list(w.values()))]+list(w.values())\n",
    "        return({\n",
    "            'loss': -np.mean(CrossScore(y, yt2pM, irtL, wtmp)),\n",
    "            'status': STATUS_OK\n",
    "            })\n",
    "    np.random.seed(seed)\n",
    "    trials = Trials()\n",
    "    fmin(Obj,\n",
    "        space=space,\n",
    "        algo=tpe.suggest,\n",
    "        max_evals=100,\n",
    "        trials=trials)\n",
    "    op = pd.concat([pd.DataFrame({\"loss\": trials.losses()}),\n",
    "                    pd.DataFrame([sum(list(trials.trials[i][\"misc\"][\"vals\"].values()), []) for i in range(len(trials))],\n",
    "                                 columns = list(trials.trials[0][\"misc\"][\"vals\"].keys()))], axis = 1)\n",
    "    return(op)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.7892\n",
      "Score: 0.7943\n",
      "Score: 0.7962\n",
      "Score: 0.8129\n",
      "Score: 0.7758\n",
      "Score: 0.7852\n",
      "Score: 0.7852\n",
      "Score: 0.7796\n",
      "Score: 0.7696\n",
      "Score: 0.7781\n",
      "Score: 0.7909\n",
      "Score: 0.7967\n",
      "Score: 0.7978\n",
      "Score: 0.8164\n",
      "Score: 0.7764\n",
      "Score: 0.7871\n",
      "Score: 0.7877\n",
      "Score: 0.7821\n",
      "Score: 0.7715\n",
      "Score: 0.7790\n",
      "Score: 0.7884\n",
      "Score: 0.7929\n",
      "Score: 0.7941\n",
      "Score: 0.8105\n",
      "Score: 0.7763\n",
      "Score: 0.7843\n",
      "Score: 0.7851\n",
      "Score: 0.7782\n",
      "Score: 0.7693\n",
      "Score: 0.7786\n",
      "Score: 0.7865\n",
      "Score: 0.7905\n",
      "Score: 0.7924\n",
      "Score: 0.8078\n",
      "Score: 0.7749\n",
      "Score: 0.7825\n",
      "Score: 0.7831\n",
      "Score: 0.7763\n",
      "Score: 0.7674\n",
      "Score: 0.7777\n",
      "Score: 0.7900\n",
      "Score: 0.7963\n",
      "Score: 0.7976\n",
      "Score: 0.8154\n",
      "Score: 0.7752\n",
      "Score: 0.7861\n",
      "Score: 0.7861\n",
      "Score: 0.7812\n",
      "Score: 0.7704\n",
      "Score: 0.7778\n",
      "Score: 0.7900\n",
      "Score: 0.7958\n",
      "Score: 0.7973\n",
      "Score: 0.8148\n",
      "Score: 0.7759\n",
      "Score: 0.7861\n",
      "Score: 0.7862\n",
      "Score: 0.7809\n",
      "Score: 0.7705\n",
      "Score: 0.7783\n",
      "Score: 0.7907\n",
      "Score: 0.7966\n",
      "Score: 0.7979\n",
      "Score: 0.8161\n",
      "Score: 0.7761\n",
      "Score: 0.7868\n",
      "Score: 0.7872\n",
      "Score: 0.7820\n",
      "Score: 0.7712\n",
      "Score: 0.7787\n",
      "Score: 0.7909\n",
      "Score: 0.7962\n",
      "Score: 0.7970\n",
      "Score: 0.8153\n",
      "Score: 0.7771\n",
      "Score: 0.7868\n",
      "Score: 0.7877\n",
      "Score: 0.7815\n",
      "Score: 0.7716\n",
      "Score: 0.7794\n",
      "Score: 0.7901\n",
      "Score: 0.7967\n",
      "Score: 0.7980\n",
      "Score: 0.8168\n",
      "Score: 0.7746\n",
      "Score: 0.7867\n",
      "Score: 0.7869\n",
      "Score: 0.7821\n",
      "Score: 0.7706\n",
      "Score: 0.7777\n",
      "Score: 0.7884\n",
      "Score: 0.7930\n",
      "Score: 0.7946\n",
      "Score: 0.8109\n",
      "Score: 0.7760\n",
      "Score: 0.7844\n",
      "Score: 0.7849\n",
      "Score: 0.7783\n",
      "Score: 0.7691\n",
      "Score: 0.7784\n",
      "Score: 0.7900\n",
      "Score: 0.7949\n",
      "Score: 0.7959\n",
      "Score: 0.8133\n",
      "Score: 0.7770\n",
      "Score: 0.7858\n",
      "Score: 0.7867\n",
      "Score: 0.7802\n",
      "Score: 0.7708\n",
      "Score: 0.7792\n",
      "Score: 0.7901\n",
      "Score: 0.7967\n",
      "Score: 0.7980\n",
      "Score: 0.8167\n",
      "Score: 0.7746\n",
      "Score: 0.7866\n",
      "Score: 0.7869\n",
      "Score: 0.7820\n",
      "Score: 0.7706\n",
      "Score: 0.7776\n",
      "Score: 0.7888\n",
      "Score: 0.7961\n",
      "Score: 0.7974\n",
      "Score: 0.8164\n",
      "Score: 0.7729\n",
      "Score: 0.7859\n",
      "Score: 0.7861\n",
      "Score: 0.7816\n",
      "Score: 0.7694\n",
      "Score: 0.7762\n",
      "Score: 0.7876\n",
      "Score: 0.7921\n",
      "Score: 0.7942\n",
      "Score: 0.8100\n",
      "Score: 0.7751\n",
      "Score: 0.7837\n",
      "Score: 0.7838\n",
      "Score: 0.7777\n",
      "Score: 0.7681\n",
      "Score: 0.7777\n",
      "Score: 0.7889\n",
      "Score: 0.7942\n",
      "Score: 0.7961\n",
      "Score: 0.8128\n",
      "Score: 0.7755\n",
      "Score: 0.7851\n",
      "Score: 0.7849\n",
      "Score: 0.7794\n",
      "Score: 0.7694\n",
      "Score: 0.7778\n",
      "Score: 0.7870\n",
      "Score: 0.7914\n",
      "Score: 0.7938\n",
      "Score: 0.8092\n",
      "Score: 0.7746\n",
      "Score: 0.7832\n",
      "Score: 0.7831\n",
      "Score: 0.7770\n",
      "Score: 0.7675\n",
      "Score: 0.7773\n",
      "Score: 0.7865\n",
      "Score: 0.7905\n",
      "Score: 0.7923\n",
      "Score: 0.8077\n",
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      "Score: 0.7766\n",
      "Score: 0.7862\n",
      "Score: 0.7867\n",
      "Score: 0.7808\n",
      "Score: 0.7709\n",
      "Score: 0.7790\n",
      "Score: 0.7906\n",
      "Score: 0.7965\n",
      "Score: 0.7978\n",
      "Score: 0.8159\n",
      "Score: 0.7761\n",
      "Score: 0.7867\n",
      "Score: 0.7870\n",
      "Score: 0.7818\n",
      "Score: 0.7712\n",
      "Score: 0.7786\n",
      "Score: 0.7906\n",
      "Score: 0.7958\n",
      "Score: 0.7963\n",
      "Score: 0.8147\n",
      "Score: 0.7773\n",
      "Score: 0.7868\n",
      "Score: 0.7878\n",
      "Score: 0.7812\n",
      "Score: 0.7716\n",
      "Score: 0.7794\n",
      "Score: 0.7896\n",
      "Score: 0.7944\n",
      "Score: 0.7957\n",
      "Score: 0.8126\n",
      "Score: 0.7766\n",
      "Score: 0.7854\n",
      "Score: 0.7860\n",
      "Score: 0.7796\n",
      "Score: 0.7703\n",
      "Score: 0.7789\n",
      "Score: 0.7903\n",
      "Score: 0.7959\n",
      "Score: 0.7967\n",
      "Score: 0.8161\n",
      "Score: 0.7767\n",
      "Score: 0.7873\n",
      "Score: 0.7882\n",
      "Score: 0.7820\n",
      "Score: 0.7716\n",
      "Score: 0.7792\n",
      "Score: 0.7907\n",
      "Score: 0.7967\n",
      "Score: 0.7979\n",
      "Score: 0.8169\n",
      "Score: 0.7759\n",
      "Score: 0.7873\n",
      "Score: 0.7878\n",
      "Score: 0.7823\n",
      "Score: 0.7714\n",
      "Score: 0.7787\n",
      "Score: 0.7909\n",
      "Score: 0.7967\n",
      "Score: 0.7978\n",
      "Score: 0.8166\n",
      "Score: 0.7764\n",
      "Score: 0.7872\n",
      "Score: 0.7879\n",
      "Score: 0.7822\n",
      "Score: 0.7716\n",
      "Score: 0.7791\n",
      "Score: 0.7902\n",
      "Score: 0.7963\n",
      "Score: 0.7977\n",
      "Score: 0.8170\n",
      "Score: 0.7754\n",
      "Score: 0.7873\n",
      "Score: 0.7879\n",
      "Score: 0.7823\n",
      "Score: 0.7711\n",
      "Score: 0.7784\n",
      "Score: 0.7908\n",
      "Score: 0.7968\n",
      "Score: 0.7980\n",
      "Score: 0.8167\n",
      "Score: 0.7761\n",
      "Score: 0.7872\n",
      "Score: 0.7877\n",
      "Score: 0.7822\n",
      "Score: 0.7714\n",
      "Score: 0.7788\n",
      "Score: 0.7907\n",
      "Score: 0.7967\n",
      "Score: 0.7979\n",
      "Score: 0.8162\n",
      "Score: 0.7761\n",
      "Score: 0.7869\n",
      "Score: 0.7872\n",
      "Score: 0.7820\n",
      "Score: 0.7713\n",
      "Score: 0.7787\n",
      "Score: 0.7877\n",
      "Score: 0.7921\n",
      "Score: 0.7941\n",
      "Score: 0.8100\n",
      "Score: 0.7752\n",
      "Score: 0.7837\n",
      "Score: 0.7840\n",
      "Score: 0.7777\n",
      "Score: 0.7682\n",
      "Score: 0.7779\n",
      "Score: 0.7902\n",
      "Score: 0.7957\n",
      "Score: 0.7972\n",
      "Score: 0.8147\n",
      "Score: 0.7761\n",
      "Score: 0.7861\n",
      "Score: 0.7864\n",
      "Score: 0.7809\n",
      "Score: 0.7707\n",
      "Score: 0.7785\n",
      "Score: 0.7905\n",
      "Score: 0.7960\n",
      "Score: 0.7974\n",
      "Score: 0.8151\n",
      "Score: 0.7764\n",
      "Score: 0.7865\n",
      "Score: 0.7868\n",
      "Score: 0.7812\n",
      "Score: 0.7710\n",
      "Score: 0.7788\n",
      "Score: 0.7899\n",
      "Score: 0.7950\n",
      "Score: 0.7964\n",
      "Score: 0.8135\n",
      "Score: 0.7765\n",
      "Score: 0.7858\n",
      "Score: 0.7862\n",
      "Score: 0.7801\n",
      "Score: 0.7705\n",
      "Score: 0.7788\n",
      "Score: 0.7907\n",
      "Score: 0.7960\n",
      "Score: 0.7970\n",
      "Score: 0.8149\n",
      "Score: 0.7769\n",
      "Score: 0.7866\n",
      "Score: 0.7873\n",
      "Score: 0.7812\n",
      "Score: 0.7714\n",
      "Score: 0.7792\n",
      "Score: 0.7905\n",
      "Score: 0.7966\n",
      "Score: 0.7979\n",
      "Score: 0.8170\n",
      "Score: 0.7757\n",
      "Score: 0.7873\n",
      "Score: 0.7879\n",
      "Score: 0.7824\n",
      "Score: 0.7713\n",
      "Score: 0.7786\n",
      "Score: 0.7909\n",
      "Score: 0.7967\n",
      "Score: 0.7979\n",
      "Score: 0.8164\n",
      "Score: 0.7763\n",
      "Score: 0.7871\n",
      "Score: 0.7877\n",
      "Score: 0.7821\n",
      "Score: 0.7715\n",
      "Score: 0.7789\n",
      "Score: 0.7895\n",
      "Score: 0.7944\n",
      "Score: 0.7960\n",
      "Score: 0.8128\n",
      "Score: 0.7762\n",
      "Score: 0.7854\n",
      "Score: 0.7857\n",
      "Score: 0.7796\n",
      "Score: 0.7700\n",
      "Score: 0.7785\n",
      "Score: 0.7902\n",
      "Score: 0.7967\n",
      "Score: 0.7980\n",
      "Score: 0.8166\n",
      "Score: 0.7747\n",
      "Score: 0.7866\n",
      "Score: 0.7869\n",
      "Score: 0.7820\n",
      "Score: 0.7707\n",
      "Score: 0.7777\n",
      "Score: 0.7901\n",
      "Score: 0.7960\n",
      "Score: 0.7972\n",
      "Score: 0.8167\n",
      "Score: 0.7759\n",
      "Score: 0.7874\n",
      "Score: 0.7881\n",
      "Score: 0.7822\n",
      "Score: 0.7713\n",
      "Score: 0.7788\n",
      "Score: 0.7908\n",
      "Score: 0.7961\n",
      "Score: 0.7968\n",
      "Score: 0.8152\n",
      "Score: 0.7772\n",
      "Score: 0.7869\n",
      "Score: 0.7877\n",
      "Score: 0.7815\n",
      "Score: 0.7717\n",
      "Score: 0.7794\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>w_1</th>\n",
       "      <th>w_2</th>\n",
       "      <th>w_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>-0.788618</td>\n",
       "      <td>0.728990</td>\n",
       "      <td>0.232908</td>\n",
       "      <td>0.038102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>-0.788616</td>\n",
       "      <td>0.784017</td>\n",
       "      <td>0.165579</td>\n",
       "      <td>0.050403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>-0.788586</td>\n",
       "      <td>0.784012</td>\n",
       "      <td>0.156592</td>\n",
       "      <td>0.059396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.788581</td>\n",
       "      <td>0.736678</td>\n",
       "      <td>0.201663</td>\n",
       "      <td>0.061658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>-0.788578</td>\n",
       "      <td>0.795136</td>\n",
       "      <td>0.148876</td>\n",
       "      <td>0.055988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>-0.788565</td>\n",
       "      <td>0.815762</td>\n",
       "      <td>0.147840</td>\n",
       "      <td>0.036398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>-0.788563</td>\n",
       "      <td>0.812743</td>\n",
       "      <td>0.136994</td>\n",
       "      <td>0.050263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-0.788559</td>\n",
       "      <td>0.761273</td>\n",
       "      <td>0.172879</td>\n",
       "      <td>0.065848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.788555</td>\n",
       "      <td>0.786718</td>\n",
       "      <td>0.148842</td>\n",
       "      <td>0.064440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>-0.788547</td>\n",
       "      <td>0.795134</td>\n",
       "      <td>0.141978</td>\n",
       "      <td>0.062888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>-0.788542</td>\n",
       "      <td>0.708028</td>\n",
       "      <td>0.233437</td>\n",
       "      <td>0.058535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-0.788537</td>\n",
       "      <td>0.800727</td>\n",
       "      <td>0.136284</td>\n",
       "      <td>0.062990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>-0.788534</td>\n",
       "      <td>0.698568</td>\n",
       "      <td>0.255587</td>\n",
       "      <td>0.045845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>-0.788531</td>\n",
       "      <td>0.826637</td>\n",
       "      <td>0.140331</td>\n",
       "      <td>0.033032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>-0.788515</td>\n",
       "      <td>0.757528</td>\n",
       "      <td>0.169622</td>\n",
       "      <td>0.072850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>-0.788507</td>\n",
       "      <td>0.823385</td>\n",
       "      <td>0.152417</td>\n",
       "      <td>0.024197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>-0.788486</td>\n",
       "      <td>0.820263</td>\n",
       "      <td>0.159480</td>\n",
       "      <td>0.020257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>-0.788484</td>\n",
       "      <td>0.821314</td>\n",
       "      <td>0.158067</td>\n",
       "      <td>0.020619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>-0.788482</td>\n",
       "      <td>0.715232</td>\n",
       "      <td>0.213334</td>\n",
       "      <td>0.071434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>-0.788465</td>\n",
       "      <td>0.820910</td>\n",
       "      <td>0.115425</td>\n",
       "      <td>0.063665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>-0.788436</td>\n",
       "      <td>0.675826</td>\n",
       "      <td>0.272706</td>\n",
       "      <td>0.051468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-0.788416</td>\n",
       "      <td>0.788860</td>\n",
       "      <td>0.202388</td>\n",
       "      <td>0.008752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>-0.788414</td>\n",
       "      <td>0.671778</td>\n",
       "      <td>0.286875</td>\n",
       "      <td>0.041347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>-0.788396</td>\n",
       "      <td>0.704287</td>\n",
       "      <td>0.280064</td>\n",
       "      <td>0.015649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.788386</td>\n",
       "      <td>0.680404</td>\n",
       "      <td>0.295400</td>\n",
       "      <td>0.024196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>-0.788373</td>\n",
       "      <td>0.810996</td>\n",
       "      <td>0.181131</td>\n",
       "      <td>0.007873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.788370</td>\n",
       "      <td>0.716726</td>\n",
       "      <td>0.197244</td>\n",
       "      <td>0.086030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>-0.788368</td>\n",
       "      <td>0.809763</td>\n",
       "      <td>0.108089</td>\n",
       "      <td>0.082148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>-0.788364</td>\n",
       "      <td>0.827252</td>\n",
       "      <td>0.162893</td>\n",
       "      <td>0.009854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>-0.788361</td>\n",
       "      <td>0.768457</td>\n",
       "      <td>0.226720</td>\n",
       "      <td>0.004822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>-0.787392</td>\n",
       "      <td>0.752088</td>\n",
       "      <td>0.084900</td>\n",
       "      <td>0.163012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.787391</td>\n",
       "      <td>0.594449</td>\n",
       "      <td>0.268884</td>\n",
       "      <td>0.136667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>-0.787340</td>\n",
       "      <td>0.606683</td>\n",
       "      <td>0.246215</td>\n",
       "      <td>0.147101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-0.787311</td>\n",
       "      <td>0.632518</td>\n",
       "      <td>0.209416</td>\n",
       "      <td>0.158065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>-0.787263</td>\n",
       "      <td>0.656741</td>\n",
       "      <td>0.176311</td>\n",
       "      <td>0.166949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>-0.787245</td>\n",
       "      <td>0.561980</td>\n",
       "      <td>0.306845</td>\n",
       "      <td>0.131175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>-0.787158</td>\n",
       "      <td>0.719051</td>\n",
       "      <td>0.101867</td>\n",
       "      <td>0.179082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.787146</td>\n",
       "      <td>0.704557</td>\n",
       "      <td>0.115940</td>\n",
       "      <td>0.179503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.787068</td>\n",
       "      <td>0.978582</td>\n",
       "      <td>0.007704</td>\n",
       "      <td>0.013714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>-0.786896</td>\n",
       "      <td>0.588669</td>\n",
       "      <td>0.236416</td>\n",
       "      <td>0.174915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-0.786888</td>\n",
       "      <td>0.649747</td>\n",
       "      <td>0.159375</td>\n",
       "      <td>0.190878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>-0.786822</td>\n",
       "      <td>0.638533</td>\n",
       "      <td>0.168463</td>\n",
       "      <td>0.193004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.786601</td>\n",
       "      <td>0.695079</td>\n",
       "      <td>0.092394</td>\n",
       "      <td>0.212527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>-0.786466</td>\n",
       "      <td>0.730046</td>\n",
       "      <td>0.050345</td>\n",
       "      <td>0.219609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.786408</td>\n",
       "      <td>0.716213</td>\n",
       "      <td>0.060431</td>\n",
       "      <td>0.223356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>-0.786352</td>\n",
       "      <td>0.553441</td>\n",
       "      <td>0.244585</td>\n",
       "      <td>0.201974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>-0.786288</td>\n",
       "      <td>0.518291</td>\n",
       "      <td>0.291094</td>\n",
       "      <td>0.190615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>-0.785820</td>\n",
       "      <td>0.565279</td>\n",
       "      <td>0.190479</td>\n",
       "      <td>0.244241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.785786</td>\n",
       "      <td>0.523688</td>\n",
       "      <td>0.244490</td>\n",
       "      <td>0.231822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.785763</td>\n",
       "      <td>0.472938</td>\n",
       "      <td>0.318962</td>\n",
       "      <td>0.208100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>-0.785481</td>\n",
       "      <td>0.545966</td>\n",
       "      <td>0.191942</td>\n",
       "      <td>0.262092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>-0.785273</td>\n",
       "      <td>0.512554</td>\n",
       "      <td>0.221360</td>\n",
       "      <td>0.266086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.785156</td>\n",
       "      <td>0.447257</td>\n",
       "      <td>0.309283</td>\n",
       "      <td>0.243459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>-0.785057</td>\n",
       "      <td>0.525622</td>\n",
       "      <td>0.188505</td>\n",
       "      <td>0.285872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.785014</td>\n",
       "      <td>0.611984</td>\n",
       "      <td>0.083361</td>\n",
       "      <td>0.304655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.785014</td>\n",
       "      <td>0.546782</td>\n",
       "      <td>0.159170</td>\n",
       "      <td>0.294047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.784419</td>\n",
       "      <td>0.532776</td>\n",
       "      <td>0.134510</td>\n",
       "      <td>0.332715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>-0.784051</td>\n",
       "      <td>0.440193</td>\n",
       "      <td>0.228934</td>\n",
       "      <td>0.330873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.783898</td>\n",
       "      <td>0.367938</td>\n",
       "      <td>0.331395</td>\n",
       "      <td>0.300667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.783897</td>\n",
       "      <td>0.384960</td>\n",
       "      <td>0.301646</td>\n",
       "      <td>0.313395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        loss       w_1       w_2       w_3\n",
       "58 -0.788618  0.728990  0.232908  0.038102\n",
       "85 -0.788616  0.784017  0.165579  0.050403\n",
       "66 -0.788586  0.784012  0.156592  0.059396\n",
       "22 -0.788581  0.736678  0.201663  0.061658\n",
       "48 -0.788578  0.795136  0.148876  0.055988\n",
       "84 -0.788565  0.815762  0.147840  0.036398\n",
       "87 -0.788563  0.812743  0.136994  0.050263\n",
       "29 -0.788559  0.761273  0.172879  0.065848\n",
       "1  -0.788555  0.786718  0.148842  0.064440\n",
       "95 -0.788547  0.795134  0.141978  0.062888\n",
       "71 -0.788542  0.708028  0.233437  0.058535\n",
       "23 -0.788537  0.800727  0.136284  0.062990\n",
       "44 -0.788534  0.698568  0.255587  0.045845\n",
       "35 -0.788531  0.826637  0.140331  0.033032\n",
       "41 -0.788515  0.757528  0.169622  0.072850\n",
       "94 -0.788507  0.823385  0.152417  0.024197\n",
       "70 -0.788486  0.820263  0.159480  0.020257\n",
       "49 -0.788484  0.821314  0.158067  0.020619\n",
       "67 -0.788482  0.715232  0.213334  0.071434\n",
       "36 -0.788465  0.820910  0.115425  0.063665\n",
       "31 -0.788436  0.675826  0.272706  0.051468\n",
       "20 -0.788416  0.788860  0.202388  0.008752\n",
       "61 -0.788414  0.671778  0.286875  0.041347\n",
       "83 -0.788396  0.704287  0.280064  0.015649\n",
       "18 -0.788386  0.680404  0.295400  0.024196\n",
       "69 -0.788373  0.810996  0.181131  0.007873\n",
       "21 -0.788370  0.716726  0.197244  0.086030\n",
       "88 -0.788368  0.809763  0.108089  0.082148\n",
       "86 -0.788364  0.827252  0.162893  0.009854\n",
       "98 -0.788361  0.768457  0.226720  0.004822\n",
       "..       ...       ...       ...       ...\n",
       "37 -0.787392  0.752088  0.084900  0.163012\n",
       "10 -0.787391  0.594449  0.268884  0.136667\n",
       "40 -0.787340  0.606683  0.246215  0.147101\n",
       "28 -0.787311  0.632518  0.209416  0.158065\n",
       "92 -0.787263  0.656741  0.176311  0.166949\n",
       "72 -0.787245  0.561980  0.306845  0.131175\n",
       "57 -0.787158  0.719051  0.101867  0.179082\n",
       "27 -0.787146  0.704557  0.115940  0.179503\n",
       "12 -0.787068  0.978582  0.007704  0.013714\n",
       "82 -0.786896  0.588669  0.236416  0.174915\n",
       "25 -0.786888  0.649747  0.159375  0.190878\n",
       "96 -0.786822  0.638533  0.168463  0.193004\n",
       "0  -0.786601  0.695079  0.092394  0.212527\n",
       "55 -0.786466  0.730046  0.050345  0.219609\n",
       "14 -0.786408  0.716213  0.060431  0.223356\n",
       "62 -0.786352  0.553441  0.244585  0.201974\n",
       "39 -0.786288  0.518291  0.291094  0.190615\n",
       "64 -0.785820  0.565279  0.190479  0.244241\n",
       "9  -0.785786  0.523688  0.244490  0.231822\n",
       "2  -0.785763  0.472938  0.318962  0.208100\n",
       "50 -0.785481  0.545966  0.191942  0.262092\n",
       "32 -0.785273  0.512554  0.221360  0.266086\n",
       "17 -0.785156  0.447257  0.309283  0.243459\n",
       "89 -0.785057  0.525622  0.188505  0.285872\n",
       "19 -0.785014  0.611984  0.083361  0.304655\n",
       "13 -0.785014  0.546782  0.159170  0.294047\n",
       "15 -0.784419  0.532776  0.134510  0.332715\n",
       "59 -0.784051  0.440193  0.228934  0.330873\n",
       "16 -0.783898  0.367938  0.331395  0.300667\n",
       "3  -0.783897  0.384960  0.301646  0.313395\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wopt = ModelWOpt(y, [yt2pxgbL, yt2pdnnL, yt2plgrL], irtL).sort_values(by = \"loss\")\n",
    "wopt = wopt.assign(w_1 = 1 - wopt.iloc[:,1:].sum(axis = 1)).sort_index(axis = 1)\n",
    "wcomp = wopt.iloc[0,1:].tolist()\n",
    "wopt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "opcomp = pd.DataFrame({'score':ModelMPredict([yvpxgbL, yvpdnnL, yvplgrL], [0.75, 0.20, 0.05])}, index = irv).reset_index().rename_axis({\"index\": \"Idx\"}, axis = 1).set_index(\"Idx\")\n",
    "opcomp.to_csv(\"{}/{}_op.csv\".format(path, title))"
   ]
  }
 ],
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